WO2024105617A1 - An automated supply chain financing system and method for improved pricing using distributed artificial intelligence - Google Patents

An automated supply chain financing system and method for improved pricing using distributed artificial intelligence Download PDF

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Publication number
WO2024105617A1
WO2024105617A1 PCT/IB2023/061604 IB2023061604W WO2024105617A1 WO 2024105617 A1 WO2024105617 A1 WO 2024105617A1 IB 2023061604 W IB2023061604 W IB 2023061604W WO 2024105617 A1 WO2024105617 A1 WO 2024105617A1
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Prior art keywords
supply chain
agents
data
artificial intelligence
financing system
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PCT/IB2023/061604
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French (fr)
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Atul Mehra
Achal MEHRA
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Atul Mehra
Mehra Achal
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Publication of WO2024105617A1 publication Critical patent/WO2024105617A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Definitions

  • the present invention relates to the field of supply chain and financing system. More particularly, the present invention is directed towards an automated supply chain financing system and method for improved pricing using distributed artificial intelligence for efficiently managing the operations related to finance such as decreasing price for borrower, increasing return on portfolio, bringing more liquidity in the system to improve business efficiency for buyers and sellers linked in a sales transaction.
  • a financial system helps in maintaining the exchange of funds and other operations and exists in forms, institutions and a supply chain finance includes a technology based solution which aims to decrease the financing costs and increase the business efficiency for both buyers and sellers who are participating in a sales transaction.
  • the currently available systems relate to finance management focusing on data integrity, visibility, efficiency to produce an error free output, further, many advancements in the financial system have been done which were focused on artificial intelligence and machine learning techniques, due to which these automated systems have shown improvement over the open account receivable systems.
  • the existing automated system do not consider the dynamic local and broader conditions in real time, which can affect the discount price.
  • the system either requires manual intervention or the existing system using automated negotiation protocol are set in limited static rule. These limitations can be understood by the fact that the negotiation strategy of the seller of the goods greatly depends upon comparison and urgency. For example, he uses the balance in his bank, borrowing money from family & friends, his own savings or through the overdraft limits that he has.
  • Seller typically makes these decisions by applying heuristics, comparing cost of funds & ease of getting the funds between various options, keeping in mind his cash flows, overall market demand, festive seasons, inventory etc. or he may not want to get it financed at all and sometimes let the payment delay if he does not get his required price.
  • Such decision making is based on human intelligence, heuristic and limited view is not very efficient and cost efficient in the short or long term.
  • CN109840847A discloses a supply chain financing method, comprising: acquisition supply chain modes purchase and sale side is acquired by invoice data and trade information builds supply chain network, related information based on core enterprise's upstream and downstream firms in supply chain network,, financing platform is built as data basis using data mart, supply chain network and configures enterprise search engine, the transaction accounting that upstream and downstream firms and core enterprise in supply chain network are calculated by invoice data assesses core enterprise's upstream and downstream firms financing need, enterprise's accrediting amount is calculated by air control model, scoring credit mode by financing platform, financing limit is determined based on financing needs, the accrediting amount, liability rating model, this obtains information of supply chain by invoice ticket information, comb supply chain network, supply chain modes related information is obtained, is graded by data model to corporate finance and provides financing limit, realize the supply chain financing business new method for going core.
  • this invention does not have provision of decision makers, due to which this invention does not provide highly accurate outcomes. Further, this invention does not work efficiently in dynamic mode.
  • US20070156584A1 discloses an electronic supply chain finance system, a method of enabling a supplier optionally to sell accounts receivable owed to the supplier, comprising receiving a payment obligation from a buyer, the payment obligation having a value and a maturity date, presenting the payment obligation to the supplier prior to the maturity date, providing the supplier with an opportunity to sell the payment obligation at a discounted value to a financial institution or other third party prior to the maturity date, and, thereafter, on the maturity date, receiving payment from the buyer for the value of the payment obligation regardless of whether the supplier sold the payment obligation prior to the maturity date.
  • this invention does not have provision of decision makers, due to which this invention does not provide highly accurate outcomes. Further, this invention does not work efficiently in dynamic mode.
  • US20070192216A1 discloses about system and method for processing particulars of a transaction over a network.
  • the system comprises a supply chain tracking module for receiving supply chain event data from at least one supply chain monitor, the supply chain data relating to the condition or location of an item along a supply chain.
  • the system also comprises a term and requirements module for receiving initial terms and requirements associated with the transaction and for generating modified terms and requirements based on supply chain event data and on at least one value algorithm, the modified terms and requirements being generated while the item is still in the supply chain.
  • this invention does not have provision of decision makers, due to which this invention does not provide highly accurate outcomes. Further, this invention does not work efficiently in dynamic mode.
  • the main object of the present invention is to provide an automated supply chain financing system and method for improved pricing using distributed artificial intelligence and human participants.
  • Yet another object of the present invention is to provide an automated supply chain financing system and method for management of invoices in hardware & software based distributed systems.
  • Still another object of the present invention is to provide an automated supply chain financing system and method for improved pricing using distributed artificial intelligence that involves machine learning and artificial intelligence strategies for automating the financing system and method.
  • the present invention is directed towards an automated supply chain financing system and method for efficiently managing the operations related to finance such as decreasing price for borrower, increasing return on portfolio for lenders, bringing more liquidity in the system.
  • the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence comprising of a plurality of computer platforms for connecting borrowers and lenders, a plurality of web servers connected with a communication network; and a plurality of modules.
  • the automated supply chain financing system is accessed by a plurality of artificial intelligence (Al) based local agents, decision maker agents, global agents and human participants.
  • the plurality of modules include a global knowledge source module and a local knowledge source module.
  • the global knowledge source module and said local knowledge source module include a data sourcing unit, a data processing unit, a prediction unit.
  • the data sourcing unit is configured to store a data related to supply chain and finance.
  • the data processing unit is configured to obtain a pre-processed data via cleaning the data via a plurality of operations.
  • the prediction unit is configured to predict an output related to one or more finance operations via processing said pre- processed data through a plurality of artificial intelligence techniques and local agents.
  • the present invention provides an automated supply chain financing system that works via a method comprising the steps of, a) establishing the communication network through the plurality of computer platforms for connecting borrowers and lenders, a plurality of artificial intelligence (Al) based agents, wherein said agents include but not limited to local agents, global agents, decision maker agents and human participants, b) connecting the plurality of web servers in the communication network, c) configuring the plurality of modules within said automated supply chain financing system, d) permitting the plurality of local agents, decision maker agents and global agents to access said automated supply chain financing system, e) storing a data related to supply chain and finance in said data sourcing unit, f) obtaining pre-processed data by cleaning the data through a plurality of operations via said data processing unit and predicting an output related to one or more finance operations by processing said pre-processed data through a plurality of artificial intelligence techniques and local agents via said prediction unit.
  • Al artificial intelligence
  • the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence comprising of a plurality of computer platforms for connecting borrowers and lenders, a plurality of Al agents, a plurality of web servers connected with a computer/ communication network and an computer engine, wherein said agents include but not limited to local agents, global agents and decision maker agents to manage operations of the system and to carry out predictive analysis, which also helps in decreasing the financing costs and improving business efficiency for buyers and sellers linked in a sales transaction.
  • the present invention provides automated supply chain financing system for improved pricing using distributed artificial intelligence, which includes a global knowledge module which apply one or more artificial intelligence techniques to predict the output.
  • the global knowledge module comprises of a data sourcing unit that includes a data related to macro-economic conditions and other, a data processing unit for processing and cleaning the data and a prediction unit that uses said artificial intelligence techniques and local agents to predict an output related to finance operations.
  • the present invention provides automated supply chain financing system for improved pricing using distributed artificial intelligence that uses distributed artificial agents and user participants to create an electronic order book for maintaining information related to stock exchanges and enables the user participants to generate and deploy one or more advanced trading strategies using hardware systems. Additionally, the order book maintains the electronic entry for buy orders or sell orders maintained in hardware caches on primary or secondary storage on servers connected through the computer network.
  • the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence that comprises of a plurality of computer platforms for connecting borrowers and lenders, a plurality of agents, a plurality of web servers connected with a computer/ communication network and an computer engine for handling operations related to finance such as decreasing the prices for the borrower, increasing return on portfolio for the lender and bringing more liquidity in the system.
  • Figure 1(a) is a block diagram of an automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
  • Figure 1(b) is another block diagram of an automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
  • Figure 2 is a block diagram of global knowledge source module of the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
  • Figure 3 is a block diagram of the local knowledge source module of the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
  • Figure 4 is a block diagram describing about the working of decision makers according to an embodiment of the present invention.
  • Figure 5 is a schematic view of the electronic order book produced by the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
  • Figure 6(a) is a graphical representation of an example scenario in the electronic order book.
  • Figures 6(b), 6(c) and 6(d) are schematic views that are representing the changes in the state of electronic order book and orders according to an embodiment of the present invention.
  • Figure 7 is a graphical representation of volatility of factors impacting decision in the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
  • Figure 8 is a graphical representation of the outcome produced by the present invention according to an embodiment of the present invention.
  • Figure 9 is a graphical representation of the prediction outcome produced by the present invention.
  • Figure 10 is a graphical representation of overall environment assessment vs auction based price in the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
  • Figure 11 is graphical representation of sales through the present invention.
  • Figures 12(a) and 12(b) are pictorial views of the example of the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention
  • the present invention provides an automated supply chain financing system and method for improved pricing using distributed artificial intelligence that facilitates dynamic decision making by using a plurality of distribution artificial intelligence agent for automating actions in the system.
  • the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence comprising of a plurality of computer platforms for connecting borrowers and lenders, a plurality of web servers connected with a communication network; and a plurality of modules.
  • the automated supply chain financing system is accessed by a plurality of artificial intelligence (Al) based local agents, decision maker agents, global agents and human participants.
  • the plurality of modules include a global knowledge source module and a local knowledge source module.
  • the global knowledge source module and said local knowledge source module include a data sourcing unit, a data processing unit, a prediction unit.
  • the data sourcing unit is configured to store a data related to supply chain and finance.
  • the data processing unit is configured to obtain a pre-processed data via cleaning the data via a plurality of operations.
  • the prediction unit is configured to predict an output related to one or more finance operations via processing said pre- processed data through a plurality of artificial intelligence techniques and local agents.
  • the present invention provides an automated supply chain financing system that works via a method comprising the steps of, a) establishing the communication network through the plurality of computer platforms for connecting borrowers and lenders, a plurality of artificial intelligence (Al) based agents, wherein said agents include but not limited to local agents, global agents, decision maker agents and human participants, b) connecting the plurality of web servers in the communication network, c) configuring the plurality of modules within said automated supply chain financing system, d) permitting the plurality of local agents, decision maker agents and global agents to access said automated supply chain financing system, e) storing a data related to supply chain and finance in said data sourcing unit, f) obtaining pre-processed data by cleaning the data through a plurality of operations via said data processing unit and predicting an output related to one or more finance operations by processing said pre-processed data through a plurality of artificial intelligence techniques and local agents via said prediction unit.
  • Al artificial intelligence
  • An automated supply chain financing system (10) for improved pricing using distributed artificial intelligence comprising of: a plurality of computer platforms (1) for connecting borrowers and lenders; a plurality of web servers connected with a communication network; and a plurality of modules; wherein: said automated supply chain financing system (10) is accessed by a plurality of artificial intelligence (Al) based local agents (2), decision maker agents (3), global agents (4) and human participants; said plurality of modules include a global knowledge source module and a local knowledge source module; said global knowledge source module and said local knowledge source module include a data sourcing unit (5), a data processing unit (6), a prediction unit (7); said data sourcing unit (5) is configured to store a data related to supply chain and finance; said data processing unit (6) is configured to obtain a pre-processed data via cleaning the data via a plurality of operations; said prediction unit (7) is configured to predict an output related to one or
  • the automated supply chain financing system (10) for improved pricing using distributed artificial intelligence comprises of a plurality of computer platforms (1) for connecting borrowers and lenders, a plurality of agents, a plurality of web servers connected with a computer/ communication network and an computer engine, wherein said artificial intelligence (Al) based agents include but not limited to local agents (2), global agents (4) and decision maker agents (3) to handle operations of the system (10) and facilitate dynamic decision making.
  • the local agents (2) is preferably local knowledge source agent and said global agent (4) is preferably a global knowledge source agent, and said agents act in semi or fully autonomous way to negotiate or quote the price using artificial intelligence. Further, shows the connection between the user participants which are spread across the network.
  • the automated supply chain financing system (10) for improved pricing using distributed artificial intelligence comprises of hardware modules connected over local area network, wide area network, internet or variations of networks such as virtual local area network or private local area network.
  • the present invention deploys these components based on caches, optimizations and configurations to control the overall performance.
  • the automated supply chain financing system (10) includes a global knowledge source module which apply one or more artificial intelligence techniques to predict the output.
  • the global knowledge source module comprises of a data sourcing unit (5) that includes a data related to stock market data, market demand data and local geographical and trends data about factors impacting various macro and micro economic conditions, a data processing unit (6) for processing and cleaning the data and a prediction unit (7) that uses said artificial intelligence techniques and local agents to predict an output related to finance operations.
  • the sources of data here preferably refer to more macro-economic conditions such as stock markets and demand and supply in the overall country or in a regional market.
  • the local knowledge source module apply one or more artificial intelligence techniques to predict the output.
  • the local knowledge source module comprises of a data sourcing unit (5) that includes a data related to inventory, cash flow, demand supply, sales, lead, management data and other, a data processing unit (6) for processing and cleaning the data and a prediction unit (7) that uses said artificial intelligence techniques and local agents to predict an output related to finance operations.
  • the sources of inputs and output herein pertain to parameters which are more local to seller and buyer here.
  • the automated supply chain financing system (10) and method for improved pricing using distributed artificial intelligence determines the discount price on an invoice, wherein the distributed artificial intelligent agents represent single or plurality of sellers or buyers or both and put a price on an invoice financing system for which the participant seeks short term lending using invoice as collateral.
  • Figure 4 is a block diagram describing about the working of decision makers according to an embodiment of the present invention, further this figure describes about the working of decision makers make decision about the discount price.
  • Another set of distributed artificial intelligent agents represents a single or plurality of financiers and further put a price for which they are ready to lend money against invoices which are aggregated or categorized by a plurality of aspects including but not limited to risk, geography, sector or time frame.
  • the invoices and price points quoted by financiers are grouped by multiple such aspects and are kept in the electronic order book which is maintained over distributed cache and systems.
  • Decision makers get the data from global and local knowledge sources about current and predictive values about factors which impact the discount price.
  • it consumes the live and historical data published by electronic order book. It then constructs an environment which is depicted in numerical value, applies artificial intelligence techniques, and uses a trading strategy which is configured for that decision maker. Based on that it comes up with a discount price. This discount price is entered into the order which is then sent to the electronic order book of invoices.
  • FIG. 5 a schematic view of the electronic order book produced by the present invention. Further, the Figure 5 depicts possible state of electronic order book maintaining invoices and the quotes provided by various lenders.
  • the data inside the order book includes the data published about the order book for example information about various bids and asks for particular dates and the movement in these prices.
  • a set of price points are determined by the distributed artificial intelligence agents or provided by a user using a manual terminal or by other means of communication such as phone. These represent the discount on the invoice that the seller of invoice and the financier are offering or bidding for respectively.
  • Figure 6(a) is a graphical representation of an example scenario in the electronic order book.
  • Figures 6(b), 6(c) and 6(d) are schematic views that are representing the changes in the state of electronic order book and orders according to an embodiment of the present invention.
  • the automated supply chain financing system (10) arranges the invoices based on distributed systems, wherein a credit score is determined by using the public financial sources of past history of the seller such as credit scores provided by bureaus, bank statements, assets and loans owned by the seller, which is optionally combined with other alternative sources of data such as utility bill payment history, social media profiling, ledger & cash flow analysis of the seller.
  • the automated supply chain financing system (10) determines a combined score by fusing all of the above information.
  • the credit score determined by the system (10) includes an auxiliary information that includes but not limited to location, sector in which the seller deals, recourse information and so on. Such information is necessary as lenders may have a specific criteria such as in case of offering a price only for a particular sector.
  • the automated supply chain financing system (10) publishes information such as best bid and best ask, current and historical information about the electronic order book. Such information is suitable then to be used by the distributed artificial intelligence agents to deduce discount prices in a supply chain financing system (10).
  • a module in the system (10) maintains a chase in hardware components by pointing to selected entries in the order book. These selected entries represent the best bid at a given credit score and the best ask for a given credit score. All such entries are combined and published to all distributed artificial intelligence agents and other modules and an example of this feature is depicted in Figures 12(a) and 12(b).
  • the distributed artificial intelligence agents subscribe to the information using a publish-subscribe method which allows, user to subscribe once and keep on receiving the information on periodic terms.
  • the distributed artificial intelligent agents determine and predict the prices by consuming the information generated by other distributed artificial intelligent agents.
  • the knowledge sources module of the present invention consume data from one or more data sources including but not limited to historical data in databases, consuming streams of data over network sockets, data lakes or through web scraping or consuming public or proprietary data. Additionally the data consumed are the ones which impact the prices, liquidity and lending rates in the market. The scope of such data is global including but not limited to larger economic conditions, interest rates, stock markets, market sentiments.
  • the agents further consume data which are more localized in nature, for instance local demand for the product, any festive season in a given geography or local geo-political economic situation in a city or state.
  • Yet another set of knowledge source distributed artificial intelligent agents are specific to the single or several participants related factors that includes but not limited to current cash flows, inventory levels, projected cash flows or similar participants.
  • the distributed artificial intelligent agents then generate an environment that is a combination of multiple factors and by using machine learning or artificial intelligence techniques the environments are compared to predict a discounting price which minimizes the cost for buyer or seller.
  • the distributed artificial intelligent agents put an aggressive price maximizing the profit, if it predicts that cash flows are not crunched and in the perceived environment, further distributed artificial intelligent agents determines that if it is possible to find a financier for the price.
  • the agents put a higher discount price for which the seller or buyer is willing to discount the bill.
  • the agent is optionally configured to enter an order on behalf of the user or it is optionally configured to take approval of the user before entering the order.
  • the agents representing one or more financiers develop strategies to maximize the return on portfolio based on various factors that impact decision making of the lender with regards to price and the urgency to deploy the funds.
  • the distributed artificial intelligent agents predicts that liquidity conditions in the market tighten next week, where the lender lend at a higher rate, then it hold on the funds for that instance and start putting higher price points on current order.
  • Such agents run multiple permutations of the outcomes to predict such scenarios.
  • the present invention provides an automated supply chain financing system (10) and method for improved pricing using distributed artificial intelligence that facilitates dynamic decision making by using a plurality of distribution artificial intelligence agent for automating actions in the system (10).
  • a graphical representation of volatility of factors impacting decision is depicted and further explains various factors i.e. global and local and the output of the global and local knowledge sources mapped over a line. It explains that factors move independently of each other.
  • the maroon line sets the invoice discount as the 60 days average of the prevalent interest rates. While the factors which impact the discount prices are moving, the 60 days average moves slowly. Thus, showing that it’s not sufficiently capturing the market forces.
  • existing supply chain financing systems either the prices are set static or at the most are left at an auction based price. Where a bank or lender changes the prices rarely, sometimes, it’s not changed for more than 60 days.
  • a graphical representation of the outcome produced by the present invention is depicted, wherein it indicates that discount prices are determined by the system (10) is closer to the 3 days moving average. For instance, if the interest rates in the market are high, however the cash flow situation for the seller is relaxed, then it is possible that he may not be willing to pay more price and may demand more discount on the invoice and vice versa.
  • FIG. 9 a graphical representation of the prediction outcome produced by the present invention is depicted, wherein this figure shows the performance of the system (10) while predicting the overall assessment of the environment based on real time data. Further, the system (10) is being able to source and process the real time data about market factors allows the system (10) to dynamically respond and predict an overall assessment of the market.
  • FIG. 10 a graphical representation of overall environment assessment vs auction based price is depicted. This figure indicates that through the present invention, the seller is able to secure better pricing even though his own cash flows were constrained and the present invention helps in make decision more rational.
  • the present invention works on the opinion that interest rates are inversely proportional to stock market index. From the present index the 3 days predicted future value of the stock market is calculated. A current value and a predicted value is calculated by the system for the sentiment score and a range from the seller is provided, by processing the pre-stored data about the interest rates & keywords are chosen by a strategy.
  • the sourcing of the data is done through an open banking layer which includes a consent manager and embedded finance modules.
  • local knowledge distribution agent in the presented outputs normalized value as micro conditions. Instead, it focuses on the factors which are more specific to the seller’s data. In the presented example the cash flows of the seller is taken into account and produce outputs a value in the range of 1 to 3.
  • the decision maker distribution artificial intelligence agents continuously takes inputs from local & global distribution artificial intelligence agent along with the order book information to do scenario constructions.
  • Such scenarios are stored in a scenario repository of the present invention.
  • the more recent data in the scenario repository is stored in RAMs of multiple servers for fast access on distributed systems, which has a configurable value. While rest is stored in the databases on secondary storage devices.
  • a moving average based strategy is used as one of the strategies.
  • the decision maker distribution artificial intelligence agents after consuming all the data sources, then compares it with the scenario repository. It then outputs a desired value of discount price on the invoice.
  • a 3 days moving average of open price is used as the baseline to estimate a price.
  • the decision maker distribution artificial intelligence agents also then uses 3 hour ticks published through order book information and uses a floor value of the low price of ticks in the last 3 hours and the price predicted by distribution artificial intelligence agents.
  • the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence and artificial intelligence/machine learning based strategies to manage the finance related operations efficiently.

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Abstract

The present invention is directed towards an automated supply chain financing system and method for improved pricing using distributed artificial intelligence that involves machine learning and artificial intelligence strategies for efficiently managing the operations related to finance such as decreasing price for borrower, increasing return on portfolio, bringing more liquidity in the system and comprises of a plurality of computer platforms (1) for connecting borrowers and lenders, a plurality of agents, a plurality of web servers connected with a computer/ communication network and an computer engine, wherein said agents include but not limited to local agents (2), global agents (4) and decision maker agents (3) to manage operations of the system and to carry out predictive analysis, which also helps in decreasing the financing costs and improving business efficiency for buyers and sellers linked in a sales transaction.

Description

“AN AUTOMATED SUPPLY CHAIN FINANCING SYSTEM AND METHOD FOR IMPROVED PRICING USING DISTRIBUTED ARTIFICIAL INTELLIGENCE”
FIELD OF THE INVENTION
The present invention relates to the field of supply chain and financing system. More particularly, the present invention is directed towards an automated supply chain financing system and method for improved pricing using distributed artificial intelligence for efficiently managing the operations related to finance such as decreasing price for borrower, increasing return on portfolio, bringing more liquidity in the system to improve business efficiency for buyers and sellers linked in a sales transaction.
BACKGROUND OF THE INVENTION
A financial system helps in maintaining the exchange of funds and other operations and exists in forms, institutions and a supply chain finance includes a technology based solution which aims to decrease the financing costs and increase the business efficiency for both buyers and sellers who are participating in a sales transaction.
With technological development, automated financial systems were introduced to automate the top finance related operations, for example, data consolidation and generation of financial statement. These automated systems helps the organizations by strategizing, processing the data to help out the user in making a strategic decision.
The currently available systems relate to finance management focusing on data integrity, visibility, efficiency to produce an error free output, further, many advancements in the financial system have been done which were focused on artificial intelligence and machine learning techniques, due to which these automated systems have shown improvement over the open account receivable systems.
In the traditional accounts receivable process, every juncture for handling finances required the manual touchpoints during the entire process, due to which systems are prone to human errors, due to which traditional open account receivable systems are replaced by the automated supply chain financing systems having improved efficiency. Further, in an automated supply chain financing system, financiers are provided with the information about the purchase in electronic form about invoice or purchase order or any other information in real time or with delay to the financiers. Multiple offers for discount rate are generated on the invoice, which the borrower can either accept or negotiate upon and in some systems automated negotiation can be deployed.
These automated systems are mostly used by companies, which are highly investment grade and typically have discounted the invoices with large amounts. However, the existing systems are not well suited for recent trends in fmtech, supply chain finance and settlement systems. Further, post covid-era, the development of systems towards fast settlement services, increased digital adoption among small and medium enterprises is gaining momentum. With the recent technological advancements in digitalisation, a trend had been seen towards a multifold increase in payments made electronically, where a major portion is from the digital payments for smaller amounts. The discount prices in the existing automated supply chain financing systems are also affected by the new use cases related to the emerging connected financial services, embedded finance and open banking that are getting much more dynamic. Hence, there arises a need for the more advanced system that facilitates the decision making on the discount price in real time.
However, the existing automated system do not consider the dynamic local and broader conditions in real time, which can affect the discount price. For this, the system either requires manual intervention or the existing system using automated negotiation protocol are set in limited static rule. These limitations can be understood by the fact that the negotiation strategy of the seller of the goods greatly depends upon comparison and urgency. For example, he uses the balance in his bank, borrowing money from family & friends, his own savings or through the overdraft limits that he has.
Seller typically makes these decisions by applying heuristics, comparing cost of funds & ease of getting the funds between various options, keeping in mind his cash flows, overall market demand, festive seasons, inventory etc. or he may not want to get it financed at all and sometimes let the payment delay if he does not get his required price. Such decision making is based on human intelligence, heuristic and limited view is not very efficient and cost efficient in the short or long term.
All such drawbacks limit the usage of automated supply chain financing systems as there is lot of human intelligence needed here which is limited by the data available to sellers, its quality and its understanding. On the lender side too, the sales and operations team of the lender is provided with the set of rules or credit decisions that are largely decided by the management team of the lender. Management team typically decides the cost considering their cost of funds, cost of operations, their own cash flows, market conditions, competition, geography, profile of customer etc. All such rules are based on human intelligence and depend on the source and quality of such data available to management.
CN109840847A discloses a supply chain financing method, comprising: acquisition supply chain modes purchase and sale side is acquired by invoice data and trade information builds supply chain network, related information based on core enterprise's upstream and downstream firms in supply chain network,, financing platform is built as data basis using data mart, supply chain network and configures enterprise search engine, the transaction accounting that upstream and downstream firms and core enterprise in supply chain network are calculated by invoice data assesses core enterprise's upstream and downstream firms financing need, enterprise's accrediting amount is calculated by air control model, scoring credit mode by financing platform, financing limit is determined based on financing needs, the accrediting amount, liability rating model, this obtains information of supply chain by invoice ticket information, comb supply chain network, supply chain modes related information is obtained, is graded by data model to corporate finance and provides financing limit, realize the supply chain financing business new method for going core. However, this invention does not have provision of decision makers, due to which this invention does not provide highly accurate outcomes. Further, this invention does not work efficiently in dynamic mode.
US20070156584A1 discloses an electronic supply chain finance system, a method of enabling a supplier optionally to sell accounts receivable owed to the supplier, comprising receiving a payment obligation from a buyer, the payment obligation having a value and a maturity date, presenting the payment obligation to the supplier prior to the maturity date, providing the supplier with an opportunity to sell the payment obligation at a discounted value to a financial institution or other third party prior to the maturity date, and, thereafter, on the maturity date, receiving payment from the buyer for the value of the payment obligation regardless of whether the supplier sold the payment obligation prior to the maturity date. However, this invention does not have provision of decision makers, due to which this invention does not provide highly accurate outcomes. Further, this invention does not work efficiently in dynamic mode.
US20070192216A1 discloses about system and method for processing particulars of a transaction over a network. The system comprises a supply chain tracking module for receiving supply chain event data from at least one supply chain monitor, the supply chain data relating to the condition or location of an item along a supply chain. The system also comprises a term and requirements module for receiving initial terms and requirements associated with the transaction and for generating modified terms and requirements based on supply chain event data and on at least one value algorithm, the modified terms and requirements being generated while the item is still in the supply chain. However, this invention does not have provision of decision makers, due to which this invention does not provide highly accurate outcomes. Further, this invention does not work efficiently in dynamic mode.
Therefore, in the light of above drawbacks, there is a need to improve the supply chain financial system that is free from human intervention, highly efficient, less time consuming, and easily implemented to real time dynamics of the changing digital world.
OBJECT OF THE INVENTION
The main object of the present invention is to provide an automated supply chain financing system and method for improved pricing using distributed artificial intelligence and human participants.
Another object of the present invention is to provide an automated supply chain financing system and method for generating an electronic order book for efficiently managing the operations related to finance. Yet another object of the present invention is to provide an automated supply chain financing system and method for determining the discount price on an invoice.
Yet another object of the present invention is to provide an automated supply chain financing system and method for management of invoices in hardware & software based distributed systems.
Still another object of the present invention is to provide an automated supply chain financing system and method for improved pricing using distributed artificial intelligence that involves machine learning and artificial intelligence strategies for automating the financing system and method.
SUMMARY OF THE INVENTION
The present invention is directed towards an automated supply chain financing system and method for efficiently managing the operations related to finance such as decreasing price for borrower, increasing return on portfolio for lenders, bringing more liquidity in the system.
In an embodiment, the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence comprising of a plurality of computer platforms for connecting borrowers and lenders, a plurality of web servers connected with a communication network; and a plurality of modules. The automated supply chain financing system is accessed by a plurality of artificial intelligence (Al) based local agents, decision maker agents, global agents and human participants. The plurality of modules include a global knowledge source module and a local knowledge source module. The global knowledge source module and said local knowledge source module include a data sourcing unit, a data processing unit, a prediction unit. The data sourcing unit is configured to store a data related to supply chain and finance. The data processing unit is configured to obtain a pre-processed data via cleaning the data via a plurality of operations. The prediction unit is configured to predict an output related to one or more finance operations via processing said pre- processed data through a plurality of artificial intelligence techniques and local agents. In an embodiment, the present invention provides an automated supply chain financing system that works via a method comprising the steps of, a) establishing the communication network through the plurality of computer platforms for connecting borrowers and lenders, a plurality of artificial intelligence (Al) based agents, wherein said agents include but not limited to local agents, global agents, decision maker agents and human participants, b) connecting the plurality of web servers in the communication network, c) configuring the plurality of modules within said automated supply chain financing system, d) permitting the plurality of local agents, decision maker agents and global agents to access said automated supply chain financing system, e) storing a data related to supply chain and finance in said data sourcing unit, f) obtaining pre-processed data by cleaning the data through a plurality of operations via said data processing unit and predicting an output related to one or more finance operations by processing said pre-processed data through a plurality of artificial intelligence techniques and local agents via said prediction unit.
In an embodiment, the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence comprising of a plurality of computer platforms for connecting borrowers and lenders, a plurality of Al agents, a plurality of web servers connected with a computer/ communication network and an computer engine, wherein said agents include but not limited to local agents, global agents and decision maker agents to manage operations of the system and to carry out predictive analysis, which also helps in decreasing the financing costs and improving business efficiency for buyers and sellers linked in a sales transaction.
In another embodiment, the present invention provides automated supply chain financing system for improved pricing using distributed artificial intelligence, which includes a global knowledge module which apply one or more artificial intelligence techniques to predict the output. Further, the global knowledge module comprises of a data sourcing unit that includes a data related to macro-economic conditions and other, a data processing unit for processing and cleaning the data and a prediction unit that uses said artificial intelligence techniques and local agents to predict an output related to finance operations.
In another embodiment, the present invention provides automated supply chain financing system for improved pricing using distributed artificial intelligence that uses distributed artificial agents and user participants to create an electronic order book for maintaining information related to stock exchanges and enables the user participants to generate and deploy one or more advanced trading strategies using hardware systems. Additionally, the order book maintains the electronic entry for buy orders or sell orders maintained in hardware caches on primary or secondary storage on servers connected through the computer network.
Still in another embodiment, the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence that comprises of a plurality of computer platforms for connecting borrowers and lenders, a plurality of agents, a plurality of web servers connected with a computer/ communication network and an computer engine for handling operations related to finance such as decreasing the prices for the borrower, increasing return on portfolio for the lender and bringing more liquidity in the system.
The above objects and advantages of the present invention will become apparent from the hereinafter set forth brief description of the drawings, detailed description of the invention, and claims appended herewith.
BRIEF DESCRIPTION OF THE DRAWING
An understanding of the an automated supply chain financing system and method for improved pricing using distributed artificial intelligence of the present invention may be obtained by reference to the following drawing:
Figure 1(a) is a block diagram of an automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
Figure 1(b) is another block diagram of an automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention. Figure 2 is a block diagram of global knowledge source module of the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
Figure 3 is a block diagram of the local knowledge source module of the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
Figure 4 is a block diagram describing about the working of decision makers according to an embodiment of the present invention.
Figure 5 is a schematic view of the electronic order book produced by the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
Figure 6(a) is a graphical representation of an example scenario in the electronic order book.
Figures 6(b), 6(c) and 6(d) are schematic views that are representing the changes in the state of electronic order book and orders according to an embodiment of the present invention.
Figure 7 is a graphical representation of volatility of factors impacting decision in the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
Figure 8 is a graphical representation of the outcome produced by the present invention according to an embodiment of the present invention.
Figure 9 is a graphical representation of the prediction outcome produced by the present invention.
Figure 10 is a graphical representation of overall environment assessment vs auction based price in the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention.
Figure 11 is graphical representation of sales through the present invention. Figures 12(a) and 12(b) are pictorial views of the example of the automated supply chain financing system for improved pricing using distributed artificial intelligence according to an embodiment of the present invention
DETAILED DESCRIPTION OF THE INVENTION
The present invention will now be described hereinafter with reference to the accompanying drawings in which a preferred embodiment of the invention is shown. This invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough, and will fully convey the scope of the invention to those skilled in the art.
Many aspects of the invention can be better understood with references made to the drawings below. The components in the drawings are not necessarily drawn to scale. Instead, emphasis is placed upon clearly illustrating the components of the present invention. Moreover, like reference numerals designate corresponding parts through the several views in the drawings. Before explaining at least one embodiment of the invention, it is to be understood that the embodiments of the invention are not limited in their application to the details of construction and to the arrangement of the components set forth in the following description or illustrated in the drawings. The embodiments of the invention are capable of being practiced and carried out in various ways. In addition, the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
The present invention provides an automated supply chain financing system and method for improved pricing using distributed artificial intelligence that facilitates dynamic decision making by using a plurality of distribution artificial intelligence agent for automating actions in the system.
In an embodiment, the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence comprising of a plurality of computer platforms for connecting borrowers and lenders, a plurality of web servers connected with a communication network; and a plurality of modules. The automated supply chain financing system is accessed by a plurality of artificial intelligence (Al) based local agents, decision maker agents, global agents and human participants. The plurality of modules include a global knowledge source module and a local knowledge source module. The global knowledge source module and said local knowledge source module include a data sourcing unit, a data processing unit, a prediction unit. The data sourcing unit is configured to store a data related to supply chain and finance. The data processing unit is configured to obtain a pre-processed data via cleaning the data via a plurality of operations. The prediction unit is configured to predict an output related to one or more finance operations via processing said pre- processed data through a plurality of artificial intelligence techniques and local agents.
In an embodiment, the present invention provides an automated supply chain financing system that works via a method comprising the steps of, a) establishing the communication network through the plurality of computer platforms for connecting borrowers and lenders, a plurality of artificial intelligence (Al) based agents, wherein said agents include but not limited to local agents, global agents, decision maker agents and human participants, b) connecting the plurality of web servers in the communication network, c) configuring the plurality of modules within said automated supply chain financing system, d) permitting the plurality of local agents, decision maker agents and global agents to access said automated supply chain financing system, e) storing a data related to supply chain and finance in said data sourcing unit, f) obtaining pre-processed data by cleaning the data through a plurality of operations via said data processing unit and predicting an output related to one or more finance operations by processing said pre-processed data through a plurality of artificial intelligence techniques and local agents via said prediction unit.
Referring to Figure 1(a), a block diagram of an automated supply chain financing system for improved pricing using distributed artificial intelligence is depicted. An automated supply chain financing system (10) for improved pricing using distributed artificial intelligence, comprising of: a plurality of computer platforms (1) for connecting borrowers and lenders; a plurality of web servers connected with a communication network; and a plurality of modules; wherein: said automated supply chain financing system (10) is accessed by a plurality of artificial intelligence (Al) based local agents (2), decision maker agents (3), global agents (4) and human participants; said plurality of modules include a global knowledge source module and a local knowledge source module; said global knowledge source module and said local knowledge source module include a data sourcing unit (5), a data processing unit (6), a prediction unit (7); said data sourcing unit (5) is configured to store a data related to supply chain and finance; said data processing unit (6) is configured to obtain a pre-processed data via cleaning the data via a plurality of operations; said prediction unit (7) is configured to predict an output related to one or more finance operations via processing said pre-processed data through a plurality of artificial intelligence techniques and local agents (2).
Referring to Figure 1(b), block diagrams of automated supply chain financing system for improved pricing using distributed artificial intelligence is depicted. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence comprises of a plurality of computer platforms (1) for connecting borrowers and lenders, a plurality of agents, a plurality of web servers connected with a computer/ communication network and an computer engine, wherein said artificial intelligence (Al) based agents include but not limited to local agents (2), global agents (4) and decision maker agents (3) to handle operations of the system (10) and facilitate dynamic decision making. The local agents (2) is preferably local knowledge source agent and said global agent (4) is preferably a global knowledge source agent, and said agents act in semi or fully autonomous way to negotiate or quote the price using artificial intelligence. Further, shows the connection between the user participants which are spread across the network.
The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence comprises of hardware modules connected over local area network, wide area network, internet or variations of networks such as virtual local area network or private local area network. The present invention deploys these components based on caches, optimizations and configurations to control the overall performance.
Referring to Figure 2, a block diagram of global knowledge source module is depicted. The automated supply chain financing system (10) includes a global knowledge source module which apply one or more artificial intelligence techniques to predict the output. Further, the global knowledge source module comprises of a data sourcing unit (5) that includes a data related to stock market data, market demand data and local geographical and trends data about factors impacting various macro and micro economic conditions, a data processing unit (6) for processing and cleaning the data and a prediction unit (7) that uses said artificial intelligence techniques and local agents to predict an output related to finance operations. Additionally, the sources of data here preferably refer to more macro-economic conditions such as stock markets and demand and supply in the overall country or in a regional market.
Referring to Figure 3, a block diagram of the local knowledge source module is depicted. The local knowledge source module apply one or more artificial intelligence techniques to predict the output. Further, the local knowledge source module comprises of a data sourcing unit (5) that includes a data related to inventory, cash flow, demand supply, sales, lead, management data and other, a data processing unit (6) for processing and cleaning the data and a prediction unit (7) that uses said artificial intelligence techniques and local agents to predict an output related to finance operations. Additionally, the sources of inputs and output herein pertain to parameters which are more local to seller and buyer here.
Further, the automated supply chain financing system (10) and method for improved pricing using distributed artificial intelligence determines the discount price on an invoice, wherein the distributed artificial intelligent agents represent single or plurality of sellers or buyers or both and put a price on an invoice financing system for which the participant seeks short term lending using invoice as collateral.
Figure 4 is a block diagram describing about the working of decision makers according to an embodiment of the present invention, further this figure describes about the working of decision makers make decision about the discount price.
Another set of distributed artificial intelligent agents represents a single or plurality of financiers and further put a price for which they are ready to lend money against invoices which are aggregated or categorized by a plurality of aspects including but not limited to risk, geography, sector or time frame. The invoices and price points quoted by financiers are grouped by multiple such aspects and are kept in the electronic order book which is maintained over distributed cache and systems. Decision makers get the data from global and local knowledge sources about current and predictive values about factors which impact the discount price. In addition, it consumes the live and historical data published by electronic order book. It then constructs an environment which is depicted in numerical value, applies artificial intelligence techniques, and uses a trading strategy which is configured for that decision maker. Based on that it comes up with a discount price. This discount price is entered into the order which is then sent to the electronic order book of invoices.
Referring to Figure 5, a schematic view of the electronic order book produced by the present invention. Further, the Figure 5 depicts possible state of electronic order book maintaining invoices and the quotes provided by various lenders. The data inside the order book includes the data published about the order book for example information about various bids and asks for particular dates and the movement in these prices. A set of price points are determined by the distributed artificial intelligence agents or provided by a user using a manual terminal or by other means of communication such as phone. These represent the discount on the invoice that the seller of invoice and the financier are offering or bidding for respectively.
Figure 6(a) is a graphical representation of an example scenario in the electronic order book. Figures 6(b), 6(c) and 6(d) are schematic views that are representing the changes in the state of electronic order book and orders according to an embodiment of the present invention.
The automated supply chain financing system (10) arranges the invoices based on distributed systems, wherein a credit score is determined by using the public financial sources of past history of the seller such as credit scores provided by bureaus, bank statements, assets and loans owned by the seller, which is optionally combined with other alternative sources of data such as utility bill payment history, social media profiling, ledger & cash flow analysis of the seller. The automated supply chain financing system (10) determines a combined score by fusing all of the above information. The credit score determined by the system (10) includes an auxiliary information that includes but not limited to location, sector in which the seller deals, recourse information and so on. Such information is necessary as lenders may have a specific criteria such as in case of offering a price only for a particular sector. The automated supply chain financing system (10) publishes information such as best bid and best ask, current and historical information about the electronic order book. Such information is suitable then to be used by the distributed artificial intelligence agents to deduce discount prices in a supply chain financing system (10). A module in the system (10) maintains a chase in hardware components by pointing to selected entries in the order book. These selected entries represent the best bid at a given credit score and the best ask for a given credit score. All such entries are combined and published to all distributed artificial intelligence agents and other modules and an example of this feature is depicted in Figures 12(a) and 12(b). The distributed artificial intelligence agents subscribe to the information using a publish-subscribe method which allows, user to subscribe once and keep on receiving the information on periodic terms.
The distributed artificial intelligent agents determine and predict the prices by consuming the information generated by other distributed artificial intelligent agents. The knowledge sources module of the present invention consume data from one or more data sources including but not limited to historical data in databases, consuming streams of data over network sockets, data lakes or through web scraping or consuming public or proprietary data. Additionally the data consumed are the ones which impact the prices, liquidity and lending rates in the market. The scope of such data is global including but not limited to larger economic conditions, interest rates, stock markets, market sentiments.
The agents further consume data which are more localized in nature, for instance local demand for the product, any festive season in a given geography or local geo-political economic situation in a city or state. Yet another set of knowledge source distributed artificial intelligent agents are specific to the single or several participants related factors that includes but not limited to current cash flows, inventory levels, projected cash flows or similar participants. The distributed artificial intelligent agents then generate an environment that is a combination of multiple factors and by using machine learning or artificial intelligence techniques the environments are compared to predict a discounting price which minimizes the cost for buyer or seller. The distributed artificial intelligent agents put an aggressive price maximizing the profit, if it predicts that cash flows are not crunched and in the perceived environment, further distributed artificial intelligent agents determines that if it is possible to find a financier for the price. On the other hand, if it finds that the seller is in more need of cash and the environment perceived is of less liquidity then the agents put a higher discount price for which the seller or buyer is willing to discount the bill. The agent is optionally configured to enter an order on behalf of the user or it is optionally configured to take approval of the user before entering the order. Similarly, the agents representing one or more financiers develop strategies to maximize the return on portfolio based on various factors that impact decision making of the lender with regards to price and the urgency to deploy the funds.
For instance, if the distributed artificial intelligent agents predicts that liquidity conditions in the market tighten next week, where the lender lend at a higher rate, then it hold on the funds for that instance and start putting higher price points on current order. Such agents run multiple permutations of the outcomes to predict such scenarios.
EXAMPLE 1
Experimentation Analysis
The present invention provides an automated supply chain financing system (10) and method for improved pricing using distributed artificial intelligence that facilitates dynamic decision making by using a plurality of distribution artificial intelligence agent for automating actions in the system (10). Referring to Figure 7, a graphical representation of volatility of factors impacting decision is depicted and further explains various factors i.e. global and local and the output of the global and local knowledge sources mapped over a line. It explains that factors move independently of each other. The maroon line sets the invoice discount as the 60 days average of the prevalent interest rates. While the factors which impact the discount prices are moving, the 60 days average moves slowly. Thus, showing that it’s not sufficiently capturing the market forces. In existing supply chain financing systems, either the prices are set static or at the most are left at an auction based price. Where a bank or lender changes the prices rarely, sometimes, it’s not changed for more than 60 days.
Referring to Figure 8, a graphical representation of the outcome produced by the present invention is depicted, wherein it indicates that discount prices are determined by the system (10) is closer to the 3 days moving average. For instance, if the interest rates in the market are high, however the cash flow situation for the seller is relaxed, then it is possible that he may not be willing to pay more price and may demand more discount on the invoice and vice versa.
Referring to Figure 9, a graphical representation of the prediction outcome produced by the present invention is depicted, wherein this figure shows the performance of the system (10) while predicting the overall assessment of the environment based on real time data. Further, the system (10) is being able to source and process the real time data about market factors allows the system (10) to dynamically respond and predict an overall assessment of the market.
Referring to Figure 10, a graphical representation of overall environment assessment vs auction based price is depicted. This figure indicates that through the present invention, the seller is able to secure better pricing even though his own cash flows were constrained and the present invention helps in make decision more rational.
For instance, even if the predicted cash flows are constrained, if the system (10) discovers that the overall market conditions are relaxed causing interest rates to drop in near term, then the borrower in the system (10) tends to ask for a higher or moderate discount. Referring to Figure 11, a graphical representation of sales through the present invention. Further, through Figure 11, it is concluded that the present invention helps the lender to discover more sales.
The present invention works on the opinion that interest rates are inversely proportional to stock market index. From the present index the 3 days predicted future value of the stock market is calculated. A current value and a predicted value is calculated by the system for the sentiment score and a range from the seller is provided, by processing the pre-stored data about the interest rates & keywords are chosen by a strategy. The sourcing of the data is done through an open banking layer which includes a consent manager and embedded finance modules. Similarly, local knowledge distribution agent in the presented outputs normalized value as micro conditions. Instead, it focuses on the factors which are more specific to the seller’s data. In the presented example the cash flows of the seller is taken into account and produce outputs a value in the range of 1 to 3. The decision maker distribution artificial intelligence agents, continuously takes inputs from local & global distribution artificial intelligence agent along with the order book information to do scenario constructions. Such scenarios are stored in a scenario repository of the present invention. The more recent data in the scenario repository is stored in RAMs of multiple servers for fast access on distributed systems, which has a configurable value. While rest is stored in the databases on secondary storage devices.
Further, in the present invention a moving average based strategy is used as one of the strategies. The decision maker distribution artificial intelligence agents, after consuming all the data sources, then compares it with the scenario repository. It then outputs a desired value of discount price on the invoice. Additionally, in the present invention a 3 days moving average of open price is used as the baseline to estimate a price. The decision maker distribution artificial intelligence agents also then uses 3 hour ticks published through order book information and uses a floor value of the low price of ticks in the last 3 hours and the price predicted by distribution artificial intelligence agents.
Therefore, the present invention provides an automated supply chain financing system for improved pricing using distributed artificial intelligence and artificial intelligence/machine learning based strategies to manage the finance related operations efficiently.
Many modifications and other embodiments of the invention set forth herein will readily occur to one skilled in the art to which the invention pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the invention is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.

Claims

CLAIMS We claim:
1. An automated supply chain financing system (10) for improved pricing using distributed artificial intelligence, comprising of: a plurality of computer platforms (1) for connecting borrowers and lenders; a plurality of web servers connected with a communication network; and a plurality of modules; wherein: said automated supply chain financing system (10) is accessed by a plurality of artificial intelligence (Al) based local agents (2), decision maker agents (3), global agents (4) and human participants; said plurality of modules include a global knowledge source module and a local knowledge source module; said global knowledge source module and said local knowledge source module include a data sourcing unit (5), a data processing unit (6), a prediction unit (7); said data sourcing unit (5) is configured to store a data related to supply chain and finance; said data processing unit (6) is configured to obtain a pre-processed data via cleaning the data via a plurality of operations; said prediction unit (7) is configured to predict an output related to one or more finance operations via processing said pre-processed data through a plurality of artificial intelligence techniques and local agents (2).
2. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1, wherein said plurality of computer platforms (1) include personal computer, mobile device, laptop.
3. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1, wherein said communication network include local area network, wide area network, internet or variations of networks such as virtual local area network or private local area network.
4. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1, wherein said local agents (2) is preferably local knowledge source agent and said global agent (4) is preferably a global knowledge source agent, and agents act in semi or fully autonomous way to negotiate or quote the price using Artificial Intelligence.
5. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1 , wherein said plurality of modules operates on the basis of a set of parameters which include caches, optimizations and configurations to control an overall performance of said automated supply chain financing system (10).
6. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1 , wherein said data sourcing unit (5) in the global knowledge source module store the data that include stock market data, market demand data and local geographical and trends data about factors impacting various macro and micro economic conditions.
7. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1, wherein said data sourcing unit (5) in the local knowledge source module stores the data that include inventory, cash flow, demand supply, sales, lead management data.
8. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1, wherein said plurality of operations include but not limited to decreasing price for borrower, increasing return on portfolio for lenders, bringing more liquidity in the system.
9. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1, wherein said plurality of artificial intelligence techniques include but not limited to artificial neural network, logistic regression, natural language processing, support vector machine.
10. The automated supply chain financing system (10) for improved pricing using distributed artificial intelligence as claimed in claim 1, wherein said automated supply chain financing system (10) works via a method comprising the steps of: a) establishing the communication network through the plurality of computer platforms for connecting borrowers and lenders, a plurality of Al agents, wherein said agents include but not limited to local agents, global agents, decision maker agents and human participants; b) connecting the plurality of web servers in the communication network; c) configuring the plurality of modules within said automated supply chain financing system (10); d) permitting the plurality of local agents, decision maker agents and global agents to access said automated supply chain financing system (10); e) storing a data related to supply chain and finance in said data sourcing unit (5); f) Obtaining pre-processed data by cleaning the data through a plurality of operations via said data processing unit (6); and g) Predicting an output related to one or more finance operations by processing said pre-processed data through a plurality of artificial intelligence techniques and local agents (2) via said prediction unit (7).
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200210922A1 (en) * 2018-12-31 2020-07-02 Noodle Analytics, Inc. Predicting a supply chain performance
US20220187847A1 (en) * 2019-11-05 2022-06-16 Strong Force Vcn Portfolio 2019, Llc Robot Fleet Management for Value Chain Networks

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200210922A1 (en) * 2018-12-31 2020-07-02 Noodle Analytics, Inc. Predicting a supply chain performance
US20220187847A1 (en) * 2019-11-05 2022-06-16 Strong Force Vcn Portfolio 2019, Llc Robot Fleet Management for Value Chain Networks

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