JP6453939B2 - Banking support system, banking support method, and banking support program - Google Patents

Description

  The present invention relates to a banking business support system, a banking business support method, and a banking business support program for supporting the provision of various services to customers in banking business.

  In the bank, specific operations such as deposit business, currency exchange business, lending business, debt guarantee, corporate bond solicitation / consignment, accompanying business such as bill underwriting, credit card, lease, credit guarantee, securities business, financial product brokerage, etc. Business is being conducted.

  In some cases, services and products are proposed to customers of financial institutions. For this reason, techniques for making efficient proposals have been studied (for example, see Patent Documents 1 to 3). In the technique described in Patent Document 1, a management computer calculates a predicted balance from a deposit / withdrawal scheduled amount based on a deposit / withdrawal history and a current balance. Then, based on the predicted balance, a notification including a transfer proposal for compensating for the shortage is performed. Moreover, in the technique described in Patent Document 2, in consideration of the household composition, an insurance suitable for this household is proposed at an appropriate timing. In this case, using the account holder data storage unit, the name identification process is executed, the deposit / withdrawal history is acquired from the deposit account data storage unit, and the expenditure prediction and income prediction are calculated using the expenditure pattern and income pattern. The future coverage is calculated using Support proposals for insurance to cover this difference. Moreover, in the technique described in Patent Document 3, the control unit of the support server supports the provision of loans to customers of financial institutions. The control unit of the support server acquires customer information and transaction history, executes loan provisional examination, and executes auction request processing, fund use confirmation processing, and loan processing when it is determined that the loan can be provided in the provisional examination.

  In addition, a technique for supporting business using machine learning or the like has been studied (see, for example, Patent Document 4). In the technique described in Patent Document 4, the information providing apparatus includes a receiving unit, a type classifying unit, a type extracting unit, and a providing unit in order to support generation of an appropriate task. The reception unit receives an answer to the requested work and a reason for determining the answer from the contractor. The type classification unit classifies the determination reason into a type based on the determination reason and the answer. The type extraction unit extracts a reason for determination belonging to a predetermined type based on the type classified by the type classification unit. The providing unit provides the reason for determination extracted by the type extracting unit.

JP 2003-323547 A JP 2007-265258 A JP 2014-157417 A JP, 2006-177477, A

  In the techniques described in the above documents 1 to 3, business support is performed for each business individually, and the connection of various information and the unification of the operating environment are not considered. For this reason, when supporting new business, it was necessary to construct a system independently. In addition, customer needs for banking operations are diverse, and it has been difficult to quickly and efficiently respond to these needs.

  A banking business support system that solves the above problems includes an account information storage unit that stores deposit / withdrawal information for each customer account, a preparatory information storage unit that records information necessary for business procedures for each customer, and a transaction And a control unit connected to the terminal and the business system. Then, the control unit generates a customer situation prediction model based on the deposit / withdrawal information recorded in the account information storage unit, predicts a future transaction in the customer using the customer situation prediction model, Pre-preparation information necessary for procedures in future transactions is recorded in the pre-preparation information storage unit, a proposal for future transactions is output to the transaction terminal, and the pre-preparation information of the proposal is sent to the business system Output business instructions using. As a result, services and products can be efficiently provided to customers and banking operations can be supported.

  According to the present invention, it is possible to provide accurate and prompt services and products to customers in banking operations.

Explanatory drawing of the bank business support system of this embodiment. FIG. 3 is an explanatory diagram of a hardware configuration example. Explanatory drawing of the process sequence of this embodiment. Explanatory drawing of the process sequence of this embodiment. Explanatory drawing of the process sequence of this embodiment. Explanatory drawing of the process sequence of this embodiment. Explanatory drawing of the process sequence of this embodiment. Explanatory drawing of the process sequence of this embodiment. Explanatory drawing of the concept of this embodiment.

  Hereinafter, an embodiment embodying a banking business support system, a banking business support method, and a banking business support program will be described with reference to FIGS. In the present embodiment, it is assumed that a service or a product is provided to a bank customer, and the service or product is provided in a timely manner. For this reason, services and products suitable for the customer are predicted based on the customer's behavior, the customer situation, and the external environment such as the world and banks. Information necessary for providing this service or product is prepared in advance.

First, the concept of the present embodiment will be described with reference to FIG. The banking business support method of the present embodiment has been studied in consideration of an automatic driving technology 500 for an automobile using artificial intelligence. In this automatic driving technique 500, the computer constructs the movement process 502 by acquiring the user's destination 501. In this movement process 502, a route candidate 503 from the current location to the destination is specified. In this route candidate 503, an external situation 504 such as a congestion situation and a road situation in each route is acquired. Further, when moving, position information 505 (current location) in the route is specified. And individual correspondence 506 in this present position is performed like recognition of a crossing person, etc. And based on the various information of route candidate 503-individual correspondence 506, automatic driving is realized by performing travel control 507.
In this embodiment, based on the automatic driving technology 500, the business support technology 510 in the bank was examined. In this business support technology 510, a computer constructs a transaction process 512 by acquiring a customer transaction purpose 511. In this transaction process 512, transaction procedure candidates 513 from the current situation until the transaction purpose is achieved are specified. In the identified transaction procedure candidate 513, an external situation 514 such as the status of services and products used in each transaction procedure is acquired. Furthermore, the customer situation 515 (for example, account balance) in the transaction procedure is specified at the time of the transaction. Individual correspondence 516 required in the transaction procedure is performed like a form used in the transaction procedure. Then, based on various information of the transaction procedure candidates 513 to the individual correspondence 516, the transaction control 517 is performed to realize automated business support.

Here, the banking business system BK1 shown in FIG. 1 is used. This banking business system BK1 includes a transaction terminal 10, a front system 11, a middle system 20, a support server 30, and a back-end system 40.
The transaction terminal 10 is a computer terminal that provides services and products for customers. For example, an automatic teller machine installed at a store of a bank store, a window terminal used by a person in charge of a window, a customer terminal that provides a banking service using the Internet, and the like can be used.

The front system 11 includes a transaction information collection unit 12, a storefront information collection unit 13, and a public information collection unit 15.
The transaction information collection unit 12 executes processing for collecting information related to transactions performed with customers. This transaction information is acquired from a transaction terminal installed at a store or a server that manages transactions using the Internet.

The storefront information collection unit 13 executes a process of collecting information about customers who have visited the storefront. In this embodiment, store information is acquired from a camera installed at the store.
The public information collection unit 15 executes processing for collecting information disclosed on the Internet or the like. As public information, market news and buzzwords and keywords used on the Internet are used. For these buzzwords and keywords, the frequency of terms used in social networking services (SNS) and various web pages is calculated, and terms that have been used frequently during the most recent predetermined period are used.

The middle system 20 functions as a business system, and includes a deposit business support unit 21, a loan business support unit 22, an exchange transaction management unit 23, a product transaction management unit 24, and various information providing units 25.
The deposit business support unit 21 executes a deposit business management process related to an account opened by a customer.
The loan business support unit 22 executes a process for managing a loan business related to a loan provided to a customer.
The exchange transaction management unit 23 executes processing for managing exchange operations such as remittance and payment of customers.
The merchandise transaction management unit 24 executes processing for managing transactions of financial products handled by the bank.
The various information providing unit 25 executes processing for managing public information for managing various information such as FAQ, support information to be output to the counter terminal used by the person in charge of the counter, and various information used for business management.

  The support server 30 includes a control unit 31, a prediction model storage unit 32, and a preliminary preparation information storage unit 33. The control unit 31 includes an information acquisition unit 311, a learning unit 312, a scoring unit 313, a prediction unit 314, and a proposal unit 315.

  The information acquisition unit 311 executes processing for collecting information related to a prediction pattern related to customer behavior from the transaction information collection unit 12 and the storefront information collection unit 13. Furthermore, the information acquisition unit 311 acquires the publicly available external information from the public information collection unit 15 via a network such as the Internet.

  The learning unit 312 performs a process of generating a prediction model of a customer, a service, and a product by machine learning of the collected customer transactions and behaviors, the status of the service, and the product. The learning unit 312 generates a prediction model used for prediction from various information by multivariate analysis, machine learning, and deep learning.

The scoring unit 313 executes processing for calculating a credit score (customer evaluation result) at the customer. The scoring unit 313 uses a variety of information recorded in the account information storage unit 43, transaction information storage unit 44, and usage status information storage unit 45 to hold a score calculation model for calculating a credit score for the customer. . This score calculation model is based on a credit score recorded in the customer information storage unit 42 based on customer attributes in the customer information storage unit 42, deposit / withdrawal information in the account information storage unit 43, and transaction history in the transaction information storage unit 44. Thus, it is generated based on an examination model for evaluating a loanable amount (financial conditions) and an executable repayment plan (interest rate, repayment period, etc.).
Based on the prediction model generated by the learning unit 312, the prediction unit 314 executes processing for predicting the behavior and situation of the customer, the situation of the service and the product.
The proposing unit 315 executes a process for proposing a service or a product in an inbound business or an outbound business based on the predicted customer behavior, service or product status.

  The prediction model storage unit 32 stores prediction model data for predicting customer behavior, service, and product trends. This prediction model data is recorded when learning is performed based on the customer situation, the service situation, and the product situation. The prediction model storage unit 32 stores prediction model data related to customers, service trend prediction model data, and product trend prediction model data.

The prediction model data relating to the customer includes data relating to the customer code and the prediction pattern.
Data relating to an identifier for identifying each customer is recorded in the customer code data area.

A prediction pattern data area, the specific and the customer data is recorded a pattern for performing the prediction of the behavior and status of the customer. Specifically, in a prediction pattern (identity identification model) for specifying a customer, a feature amount of the identity identification image such as the identity image of the customer or a certificate image of the identity is recorded. In addition to the identity identification image, it is also possible to use feature quantities such as the user's voice and the behavior of the customer by moving images. In addition, in a prediction pattern (customer situation prediction model) for predicting behaviors and situations, conditions (variables) when the customer uses each service or product are recorded.

The service trend prediction model data includes data related to service codes and trend patterns.
Data relating to an identifier for specifying each service is recorded in the service code data area.

  In the trend pattern data area, data relating to a pattern for predicting the trend of the service is recorded. Specifically, conditions (variables) when the contents of service (for example, exchange rate or fee) fluctuate are recorded.

The product trend prediction model data includes data on product codes and trend patterns.
Data relating to an identifier for specifying each product is recorded in the product code data area.

  In the trend pattern data area, data relating to a pattern for predicting the trend of the product is recorded. Specifically, a condition (variable) when the contents (for example, price or position) of the product fluctuate is recorded. This trend pattern is generated in consideration of not only future market conditions but also future bank management conditions.

  The pre-preparation information storage unit 33 stores pre-preparation data for information necessary when providing services and products to customers. This advance preparation data is recorded when a customer's behavior is predicted. The advance preparation data includes data related to customer code, service type, prediction date, trigger condition, and preparation information.

Data relating to an identifier for identifying each customer is recorded in the customer code data area.
In the service type data area, data relating to an identifier for identifying a service that the customer may use is recorded.

In the predicted date data area, data related to the date and time when the prediction is performed is recorded.
In the trigger condition data area, data related to the time when the use of the service and the product is predicted in the inbound business and the condition in the case of proposing the service and the product in the outbound business are recorded.

  In the preparation information data area, information necessary for services and products provided to customers is recorded. For example, when providing a specific service or product to a specific customer, information necessary to provide this service or product, a form requesting the customer to fill in, a message required when using the service or selling the product, etc. Prepare.

The back-end system 40 includes a back-end management unit 41, a customer information storage unit 42, an account information storage unit 43, a transaction information storage unit 44, a usage status information storage unit 45, a service information storage unit 47, a product information storage unit 48, a management An information storage unit 49 is provided.
The back-end management unit 41 manages information recorded in the customer information storage unit 42 to the management information storage unit 49.

  The customer information storage unit 42 stores customer management records for bank customers. The customer management record is registered when a customer opens an account with a financial institution, and is updated when the customer changes. In the customer management record, data related to customer code, customer information, customer attributes, account identifiers, and credit scores are recorded.

In the customer code data area, data relating to an identifier for identifying each customer of the financial institution is recorded.
In the customer information data area, data relating to personal information (name, account identifier, contact information, address, etc.) of the customer is recorded.

In the customer attribute data area, data relating to the customer attributes (age, gender, job type, etc.) is recorded.
In the account identifier data area, data relating to an identifier (main branch code, type code, account number) for specifying an account held by the customer is recorded.
In the credit score data area, data relating to a value obtained by evaluating the credit level for the customer is recorded.

  In the account information storage unit 43, an account management record for managing a transaction using an account opened by a customer is recorded. The account management record is registered when an account is opened in a financial institution, and is updated at the time of transactions using the account. In this account management record, data relating to an account identifier, an account balance, and an account history is recorded.

Data relating to an identifier for identifying each account is recorded in the account identifier data area.
Data relating to the balance of this account is recorded in the account balance data area.
In the account history data area, data related to the date and time of deposit / withdrawal, the amount of money, a summary, etc. are recorded for the deposit and withdrawal in this account.

  The transaction information storage unit 44 records a transaction management record for managing transactions with customers. The transaction management record is registered when an account is opened in a financial institution and is updated according to the transaction. In this transaction management record, data relating to the customer code and transaction history is recorded.

Data relating to an identifier for identifying each customer is recorded in the customer code data area.
In the transaction history data area, data related to the transaction date and time, transaction type, transaction amount, and transaction place is recorded for the transaction performed with the customer. For the transaction type, an identifier for specifying the financial product purchased at this financial institution is recorded. For the transaction place, an identifier for identifying the store where the transaction was performed is recorded.

  The usage status information storage unit 45 records a service usage status management record for managing the usage status of services and products used by customers. This service usage status management record is recorded when information about customer behavior is acquired from the transaction information collection unit 12 or the storefront information collection unit 13. In the service usage status management record, data relating to the customer code, date / time, location, usage service, and customer status is recorded.

In the customer code data area, data relating to an identifier for identifying each customer who uses the service is recorded.
In the date / time and location data area, the date and time when the customer used the service, and data related to the location are recorded.

  In the use service data area, an identifier for specifying the service used by the customer is recorded. Use services include, for example, deposit business, loan business, currency exchange business, buying and selling of financial products, and inquiry.

  In the customer status data area, data related to the customer status when the customer uses the service is recorded. As the customer situation, for example, a feature amount calculated from a customer image or behavior when using the service can be used.

  The service information storage unit 47 records a service management record related to a service provided by the bank. This service management record is recorded when information regarding the status of services provided by a bank is acquired. In the service management record, data relating to the service code, date and time, and provision status is recorded.

Data relating to an identifier for specifying each service is recorded in the service code data area.
In the date and time data area, data related to the date and time indicating the service provision status is recorded.
In the provision status data area, data relating to the provision status (for example, fee and rate) of this service is recorded.

  The merchandise information storage unit 48 records a merchandise management record related to the operational status of financial products provided by the bank. This merchandise management record is recorded when information about the status of financial products operated by a bank is acquired. In the merchandise management record, data relating to the merchandise code, date / time, operation status, and position is recorded.

Data relating to an identifier for identifying each financial product is recorded in the product code data area.
In the date and time data area, data relating to the date and time indicating the operational status of the financial product is recorded.

In the operation status data area, data relating to the operation status (for example, product price) of the financial product is recorded.
In the position data area, data relating to the position (for example, the amount held) of the financial product is recorded.

  In the management information storage unit 49, a management management record relating to the management status in the bank is recorded. This business management record is recorded when business information is acquired. In the business management record, data related to time and business indicators are recorded.

In the time data area, data related to the time when the management information is created (for example, the closing date such as the closing date, daily, weekly, monthly, etc.) is recorded.
In the management index data area, data relating to values representing the management status is recorded. As the management index, financial statements and values related to income and expenditure on the closing date can be used.

(Hardware configuration example)
FIG. 2 is a hardware configuration example of the information processing apparatus H10 that functions as the transaction terminal 10 to the back-end system 40 or the like.

  The information processing device H10 includes a communication interface H11, an input device H12, a display device H13, a storage unit H14, and a processor H15. Note that this hardware configuration is an example, and other hardware may be included.

  The communication interface H11 is an interface that establishes a communication path with another device and executes data transmission / reception, and is, for example, a network interface card or a wireless interface.

The input device H12 is a device that receives input from a user or the like, and is, for example, a mouse or a keyboard.
The display device H13 is a display or a touch panel that displays various types of information.

  The storage unit H14 stores data and various programs for executing various functions of the transaction terminal 10 to the back-end system 40 (for example, a prediction model storage unit 32, a preliminary preparation information storage unit 33, a customer information storage unit). 42 to management information storage unit 49). Examples of the storage unit H14 include a ROM, a RAM, and a hard disk.

  The processor H15 controls each process in the transaction terminal 10 to the back-end system 40 using programs and data stored in the storage unit H14. Examples of the processor H15 include a CPU and an MPU. The processor H15 develops a program stored in the ROM or the like on the RAM and executes various processes corresponding to various processes. For example, when the banking business support program is activated, the processor H15 operates processes for executing processes shown in FIGS.

(Overview)
Next, the outline of the business support process will be described with reference to FIG. This business support process is periodically executed.

  First, the control unit 31 of the support server 30 executes a service status and product status learning process (step S1-1). Specifically, the information acquisition unit 311 of the control unit 31 obtains status information (service usage status, product status, product status) from the service information storage unit 47, product information storage unit 48, and management information storage unit 49 for each service and product. Management status). And the learning part 312 produces | generates the prediction pattern of a service condition for every service and goods based on the acquired condition information. Then, the learning unit 312 records the generated prediction pattern in the prediction model storage unit 32 in association with the service code and the product code.

  Next, the control unit 31 of the support server 30 executes a service and product trend prediction process (step S1-2). Specifically, the prediction unit 314 of the control unit 31 predicts service trends and product trends according to future times based on the prediction patterns recorded in the prediction model storage unit 32.

  Further, the control unit 31 of the support server 30 executes a customer behavior learning process (step S1-3). Specifically, the information acquisition unit 311 of the control unit 31 performs, from the account information storage unit 43, the transaction information storage unit 44, and the usage status information storage unit 45, for each customer. Status, service usage status). And the learning part 312 produces | generates the prediction pattern for every customer based on the acquired action condition. Then, the learning unit 312 records the generated prediction pattern in the prediction model storage unit 32 in association with the customer code.

Next, the control unit 31 of the support server 30 executes a customer behavior prediction process (step S1-4). Specifically, the prediction unit 314 of the control unit 31 predicts a service use time and a product purchase condition (trigger condition) for the customer based on the prediction model recorded in the prediction model storage unit 32.
"Service status and product status learning process (step S1-1), service and product trend prediction process (step S1-2)", "customer behavior learning process (step S1-3), customer behavior" The order of “prediction processing (step S1-4)” is not limited. Customer behavior may be predicted after predicting service and product trends, or service and product trends may be predicted after predicting customer behavior. Further, it may be performed in parallel with the same period.

  Next, the control part 31 of the support server 30 performs a preliminary preparation process (step S1-5). Specifically, the prediction unit 314 of the control unit 31 predicts information necessary when the customer uses a service or a product whose use is predicted. Then, the prediction unit 314 associates the predicted preparation information with the customer code, service type, prediction date (current date and time), provision time (service use time or product purchase time), and prepares the preparatory information storage unit. 33.

  Next, the control part 31 of the support server 30 performs a proposal process (step S1-6). Specifically, the proposal unit 315 of the control unit 31 proposes services and products to the customer at the provision time. This proposal may be performed in an outbound business or an inbound business.

  Next, the control unit 31 of the support server 30 executes a request process (step S1-7). Specifically, the proposal unit 315 of the control unit 31 accepts a request for service use or product purchase from a customer in response to the proposal.

  In this case, the control unit 31 of the support server 30 executes a current state confirmation process (step S1-8). Specifically, the suggestion unit 315 of the control unit 31 performs a current status check as to whether or not the preparation information recorded in the advance preparation information storage unit 33 can be used. When the situation has changed, the proposing unit 315 adjusts the preparation information according to the current state.

  Then, the control unit 31 of the support server 30 executes business support processing (step S1-9). Specifically, the suggestion unit 315 of the control unit 31 provides the middle system 20 with the preparation information recorded in the advance preparation information storage unit 33 or the adjusted preparation information. In this case, the middle system 20 provides services and products according to requests from customers using information prepared in advance.

(Specific examples of business support processing)
Hereinafter, a specific example of the business support process will be described with reference to FIGS. As business support processing, exchange business support processing, loan business support processing, product sales support processing, inquiry support support processing, and store visit support processing will be described.
(Foreign exchange business support processing)
The exchange transaction support process will be described with reference to FIG.
First, the control unit 31 of the support server 30 executes service prediction processing (step S2-1). Specifically, the prediction unit 314 of the control unit 31 predicts an exchange transaction request (for example, a predicted remittance service use timing) using a customer account based on the prediction pattern recorded in the prediction model storage unit 32. To do.

  Next, the control part 31 of the support server 30 performs a service preparation process (step S2-2). Specifically, the prediction unit 314 of the control unit 31 acquires information necessary for the exchange business from the exchange transaction management unit 23. In the exchange business, information on a remittance procedure (remittance route) for remittance from a customer account to a remittance destination account is acquired. Next, the prediction unit 314 predicts the external situation in the remittance procedure using the service trend prediction model data recorded in the prediction model storage unit 32. As an external situation, for example, in the case of foreign remittance, the exchange rate in the destination country is predicted. Further, the prediction unit 314 predicts the customer situation in the remittance procedure using the prediction model data of the customer situation recorded in the prediction model storage unit 32. As the customer situation, for example, the balance of the customer account is predicted. Then, the prediction unit 314 predicts a form necessary for the transaction and prepares the form. For example, as a form, an application form necessary for a remittance request, or a request form for compensating the remittance amount when a balance shortage is predicted are prepared. Then, the prediction unit 314 records the generated preparation information in the preparation information storage unit 33 in association with the customer code, the prediction date, and the trigger condition.

  Next, the control part 31 of the support server 30 performs a service proposal process (step S2-3). Specifically, the proposal unit 315 of the control unit 31 performs a proposal process for the customer. The timing for making this proposal is assumed to be based on the trigger condition recorded in the advance preparation information storage unit 33, based on the outbound business, or based on the detection of the customer's visit to the inbound business.

  Next, the control unit 31 of the support server 30 executes a service request acquisition process (step S2-4). Specifically, the proposal unit 315 of the control unit 31 receives a business request regarding a proposal from a customer.

  Next, the control part 31 of the support server 30 performs the confirmation process of the customer present condition in a procedure (step S2-5). Specifically, the proposal unit 315 of the control unit 31 checks the current state of the advance preparation information for the procedure corresponding to the business request. When the situation changes with respect to the preparation information recorded in the advance preparation information storage unit 33, the proposing unit 315 adjusts the preparation information according to the current state.

  When the request from the customer is received, the control unit 31 of the support server 30 executes a business support process (step S2-6). Specifically, the proposal unit 315 of the control unit 31 provides the preparation information recorded in the advance preparation information storage unit 33 or adjusted preparation information to the middle system 20, and the account withdrawal in the deposit business support unit 21. Instructs remittance in the exchange transaction management unit 23.

(Lending business support processing)
The loan business support process will be described with reference to FIG.
First, the control unit 31 of the support server 30 executes service prediction processing (step S3-1). Specifically, the prediction unit 314 of the control unit 31 predicts a loan business request based on the prediction pattern recorded in the prediction model storage unit 32. For example, by using the prediction model data of the customer behavior and the customer situation recorded in the prediction model storage unit 32, the amount required for the customer behavior and the balance in the customer account are predicted. Then, the prediction unit 314 predicts the possibility of using the loan based on the predicted required amount and the account balance. In this case, when the account balance is low with respect to the predicted required amount, a high value is calculated as the possibility of using the loan (prediction accuracy).

  Next, the control part 31 of the support server 30 performs a service preparation process (step S3-2). Specifically, the prediction unit 314 of the control unit 31 prepares information necessary for the loan business from the loan business support unit 22. In the lending business, information on a lending procedure for providing a loan to a customer is acquired from the lending business support unit 22. Next, the prediction unit 314 predicts the external situation in the lending procedure using the service trend prediction model data recorded in the prediction model storage unit 32. As an external situation, for example, an interest rate for a loan is predicted. Then, the prediction unit 314 predicts information necessary for lending. Here, the scoring unit 313 calculates the credit score of this customer as information necessary for lending. Then, the scoring unit 313 includes various information recorded in the account information storage unit 43, the transaction information storage unit 44, and the usage status information storage unit 45 according to the prediction accuracy corresponding to the loan availability of the customer. Then, the information necessary for scoring is extracted. For example, when the prediction accuracy is high, more detailed information is extracted in order to perform strict scoring than when the prediction accuracy is low. And the scoring part 313 calculates the credit score of this customer using the extracted information. Next, the prediction unit 314 creates a form or the like necessary for lending according to the credit score. Then, the prediction unit 314 records the generated preparation information in the preparation information storage unit 33 in association with the customer code.

  Next, similarly to steps S2-3 to S2-5, the control unit 31 of the support server 30 performs a service proposal process (step S3-3), a service request acquisition process (step S3-4), and the customer current status in the procedure. The confirmation process (step S3-5) is executed.

  Next, the control unit 31 of the support server 30 executes a business support process (step S3-6). Specifically, the proposal unit 315 of the control unit 31 provides the preparation information recorded in the advance preparation information storage unit 33 or the adjusted preparation information to the middle system 20, and the exchange operation in the loan operation support unit 22. Give instructions.

(Product sales support processing)
The product sales support process will be described with reference to FIG. Here, although it is assumed that merchandise is sold to customers, it may be possible to support investment bank-like business in which a bank conducts transactions in the market. In this case, in the following processing, “customer” is replaced with “bank” and executed.
First, the control unit 31 of the support server 30 executes a product trend prediction process (step S4-1). Specifically, the prediction unit 314 of the control unit 31 predicts a product trend according to a future time based on a product trend prediction model recorded in the prediction model storage unit 32. Here, the operational status and value of future products are predicted. In this case, using the position recorded in the merchandise information storage unit 48 and the management information (management index) recorded in the management information storage unit 49, the trend of the product is predicted based on the transaction status and management status of the bank. It is also possible to do this.

  Next, the control part 31 of the support server 30 performs a transaction prediction process (step S4-2). Specifically, the prediction unit 314 of the control unit 31 predicts the possibility of the customer's product purchase based on the prediction model of the customer situation recorded in the prediction model storage unit 32. In this case, a condition for proposing a product is recorded in the advance preparation information storage unit 33 as a trigger condition.

  Next, the control part 31 of the support server 30 performs a transaction preparation process (step S4-3). Specifically, the prediction unit 314 of the control unit 31 acquires information necessary for product purchase from the product transaction management unit 24. In purchasing a product, information on a product description, a prospectus, and a purchase application form is acquired. Then, the prediction unit 314 uses the customer information to record the generated advance preparation information in the advance preparation information storage unit 33 in association with the customer code.

  Next, the control part 31 of the support server 30 performs a product proposal process (step S4-4). Specifically, the suggestion unit 315 of the control unit 31 acquires various information recorded in the customer information storage unit 42 to the management information storage unit 49 and satisfies the trigger condition recorded in the advance preparation information storage unit 33. Determine whether or not. When the proposal unit 315 determines that the trigger condition is satisfied, the proposal unit 315 makes a product proposal to the customer by an outbound operation. In this case, a call is made to the customer's contact information by sending a product proposal email or by voice synthesis of the product proposal. Moreover, you may make it output the instruction | indication of an outbound operation | work with respect to a customer person in charge.

  Next, the control unit 31 of the support server 30 executes a purchase request acquisition process (step S4-5). Specifically, the proposal unit 315 of the control unit 31 receives a product purchase request related to the proposal from the customer.

  Next, the control unit 31 of the support server 30 executes a product status and customer status confirmation process (step S4-6). Specifically, the proposal unit 315 of the control unit 31 checks the current state of the advance preparation information for the procedure corresponding to the product purchase. Here, the customer information storage unit 42, the account information storage unit 43, the transaction information storage unit 44, and the usage status information storage unit 45 are used to check the customer status, and the product information storage unit 48 is used to check the product status. Do. When the situation changes from the preparation information recorded in the advance preparation information storage unit 33, the proposal unit 315 adjusts the preparation information according to the current situation.

  Next, the control unit 31 of the support server 30 executes business support processing (step S4-7). Specifically, the proposal unit 315 of the control unit 31 provides the preparation information recorded in the advance preparation information storage unit 33 or adjusted preparation information to the middle system 20, and the account withdrawal in the deposit business support unit 21. The merchandise transaction management unit 24 instructs merchandise sales.

(Inquiry support processing)
The inquiry support support process will be described with reference to FIG.
First, the control unit 31 of the support server 30 executes trend information collection processing (step S5-1). Specifically, the information acquisition unit 311 of the control unit 31 acquires published external information (trend information) from the public information collection unit 15. Here, as trend information, for example, keywords that are popular (buzzwords) or brands that change greatly in the market situation are acquired by increasing the frequency of appearance at each site published on the Internet.

  Next, the control unit 31 of the support server 30 executes a related service / product prediction process corresponding to the trend (step S5-2). Specifically, the prediction unit 314 of the control unit 31 uses the deposit business support unit 21 to the product transaction management unit 24 to specify services and products related to trend information (buzzwords, brands, etc.).

  Next, the control unit 31 of the support server 30 executes a preparatory process for related information (step S5-3). Specifically, the prediction unit 314 of the control unit 31 generates an answer for responding to an inquiry related to trend information (buzzwords, brands, etc.) for related services and products. In this case, trend information, information on related services and products may be output to the person-in-charge terminal, and the preparation of an answer corresponding to the inquiry may be instructed. Further, the prediction unit 314 may predict the return of the financial product using the product trend prediction model recorded in the prediction model storage unit 32 based on the trend information. Then, the prediction unit 314 records the advance preparation information in the advance preparation information storage unit 33. As for FAQs, the various information providing unit 25 uploads answers to questions using the FAQ public page as the advance preparation information storage unit 33.

  Next, the control unit 31 of the support server 30 executes an inquiry acquisition process (step S5-4). Specifically, the proposal unit 315 of the control unit 31 receives an inquiry from a customer terminal.

  Next, the control part 31 of the support server 30 performs the present confirmation process (step S5-5). Specifically, the proposal unit 315 of the control unit 31 confirms the current state of trend information. And when the situation has changed, the proposal part 315 adjusts preparation information according to the present condition.

  Next, the control unit 31 of the support server 30 executes an answer process for the inquiry (step S5-6). Specifically, the proposal unit 315 of the control unit 31 provides the preparation information recorded in the advance preparation information storage unit 33 or the adjusted preparation information to the various information providing unit 25.

(Visit store support processing)
The store visit support process will be described with reference to FIG. In this case, using the customer's access as a trigger, the prepared advance information is used to provide services and products.

  First, the control unit 31 of the support server 30 executes an access image acquisition process (step S6-1). Specifically, the information acquisition unit 311 of the control unit 31 acquires the store visitor image from the storefront information collection unit 13.

  Next, the control unit 31 of the support server 30 executes store visitor identification processing (step S6-2). Specifically, the prediction unit 314 of the control unit 31 uses the prediction model related to the customer recorded in the prediction model storage unit 32 to identify the store visitor and specify the customer code.

  Next, the control unit 31 of the support server 30 performs a store visit prediction process for the store visitor (step S6-3). Specifically, the prediction unit 314 of the control unit 31 acquires, from the advance preparation information storage unit 33, advance preparation data that is associated with the customer code of the store visitor and the current date and time is included in the provision conditions. At this stage, the prediction unit 314 may predict the customer's purpose of visiting the store using the prediction model of customer behavior recorded in the prediction model storage unit 32.

  Next, the control part 31 of the support server 30 performs the transaction proposal process according to store visit purpose prediction (step S6-4). Specifically, the suggestion unit 315 of the control unit 31 identifies the transaction type (service or product type) recorded in the advance preparation data acquired from the advance preparation information storage unit 33. And the proposal part 315 outputs the transaction proposal regarding the specified transaction type to a shop front terminal.

  Next, the control unit 31 of the support server 30 executes a store visit purpose specifying process (step S6-5). Specifically, the suggestion unit 315 of the control unit 31 acquires the store visit purpose (transaction type) input at the storefront terminal via the storefront information collection unit 13.

  Next, the control part 31 of the support server 30 performs the acquisition process of the personal identification information according to the transaction type (step S6-6). Specifically, the proposal unit 315 of the control unit 31 acquires the personal identification information necessary for this transaction from the deposit business support unit 21 to the product transaction management unit 24.

  Next, the control part 31 of the support server 30 performs an identity verification process (step S6-7). Specifically, the proposal unit 315 of the control unit 31 performs identity verification using the identity verification information necessary for the transaction. For example, an image of the driver's license presented by the person is acquired, and the authenticity of the driver's license is confirmed using a prediction model related to the customer recorded in the prediction model storage unit 32. The proposal unit 315 generates identity verification information when the authenticity of the driver's license can be confirmed.

  Next, the control unit 31 of the support server 30 executes a business support process (step S6-8). Specifically, the proposal unit 315 of the control unit 31 provides the preparation information recorded in the advance preparation information storage unit 33 to the middle system 20 and instructs the transaction together with the identity confirmation information.

As described above, according to the present embodiment, the following effects can be obtained.
(1) In this embodiment, the control unit 31 of the support server 30 executes a service status and product status learning process (step S1-1) and a service and product trend prediction process (step S1-2). As a result, the future situation of services and products can be predicted, and accurate and timely proposals can be efficiently made to customers.

  (2) In the present embodiment, the control unit 31 of the support server 30 executes customer behavior learning processing (step S1-3) and customer behavior prediction processing (step S1-4). This makes it possible to efficiently make timely and accurate proposals according to the customer's situation.

  (3) In the present embodiment, the control unit 31 of the support server 30 performs pre-preparation processing (step S1-5), proposal processing (step S1-6), request processing (step S1-7), business support processing (step S1-9) is executed. Thereby, when a request from a customer is received, it can respond quickly.

  (4) In the present embodiment, the control unit 31 of the support server 30 executes exchange transaction support processing. Thereby, according to the situation, it is possible to accurately perform the necessary procedures in the exchange business. For example, an appropriate procedure can be performed even when the remittance route changes based on the exchange rate or the like, and the procedure according to the remittance route is different.

  (5) In the present embodiment, the control unit 31 of the support server 30 executes a loan business support process. Thereby, an accurate loan proposal and loan can be performed. For example, even when a customer needs to be scored in lending, the lending judgment can be made efficiently using the advance preparation information.

(6) In the present embodiment, the control unit 31 of the support server 30 executes an inquiry response support process. Thereby, it is possible to respond to the inquiry quickly and efficiently.
(7) In this embodiment, the control unit 31 of the support server 30 executes store visit support processing. Thereby, a visitor can be specified efficiently and the proposal corresponding to the visitor can be performed.

Moreover, you may change the said embodiment into the following aspects.
In the above-described embodiment, the case where the present invention is applied to the currency exchange business support process to the store visit support process has been described as a specific example of the business support process. The application target is not limited to these. A prediction model can be generated based on learning of various types of information, and the prediction model can be used to support specific work, accompanying work, and peripheral work for performing prediction.

  In the above embodiment, the control unit 31 of the support server 30 executes a service status and product status learning process (step S1-1) and a service and product trend prediction process (step S1-2). Further, the control unit 31 of the support server 30 executes customer behavior learning processing (step S1-3) and customer behavior prediction processing (step S1-4). It is not necessary to use all of the learning and prediction targets as long as any of service, product trends, and customer information is used. Further, other elements (for example, economic and market trends) may be used in combination.

  In the above embodiment, the control unit 31 of the support server 30 executes a customer behavior learning process (step S1-3) and a customer behavior prediction process (step S1-4). Here, for the learning and prediction target customers, a prediction model may be generated using information on customers belonging to the same customer attribute. In this case, the learning unit 312 of the control unit 31 generates a statistical prediction model for each customer attribute of the customer recorded in the customer information storage unit 42. Then, the learning unit 312 applies a statistical prediction model to the customer with a small amount of information accumulated in the account information storage unit 43 to the usage situation information storage unit 45 and records the statistical prediction model in the prediction model storage unit 32. .

  In the above embodiment, the control unit 31 of the support server 30 periodically performs the business support process. The execution timing of the business support process is not limited to this. For example, when the service information storage unit 47 to the management information storage unit 49 detect a change in information about a service or a product, the control unit 31 of the support server 30 learns the product status and the service status (step S1-1). ), Service and product trend prediction processing (step S1-2). Further, when the control unit 31 of the support server 30 detects a change in information about the customer in the customer information storage unit 42 to the usage status information storage unit 45, the customer behavior learning process (step S1-3), the customer behavior The prediction process (step S1-4) is executed. And the control part 31 of the support server 30 respond | corresponds to the prediction process (step S1-2) of a service and goods, and the preparatory process (step S1-5) according to the prediction process (step S1-4) of a customer action. Execute.

  In the above embodiment, the control unit 31 of the support server 30 executes store visit support processing when a store visitor is detected at the store. Here, the present invention is not limited to visits to actual stores, but can also be applied when logging in to a net banking service that can be connected via the Internet.

  BK1 ... Banking business system, 10 ... Transaction terminal, 11 ... Front system, 12 ... Transaction information collection unit, 13 ... Store information collection unit, 15 ... Public information collection unit, 20 ... Middle system, 21 ... Deposit business support unit, 22 ... loan business support part, 23 ... exchange transaction management part, 24 ... merchandise transaction management part, 25 ... various information providing parts, 30 ... support server, 31 ... control part, 311 ... information acquisition part, 312 ... learning part, 313 ... Prediction unit, 314 ... scoring unit, 315 ... proposal unit, 32 ... prediction model storage unit, 33 ... advance preparation information storage unit, 40 ... back-end system, 41 ... back-end management unit, 42 ... customer information storage unit, 43 ... account information storage unit, 44 ... transaction information storage unit, 45 ... usage status information storage unit, 47 ... service information storage unit, 48 ... merchandise information storage unit, 49 ... management information storage unit.

Claims (6)

An account information storage unit for storing deposit / withdrawal information for each customer account;
A product information storage unit for storing product information of financial products provided by the bank;
For each customer, a pre-preparation information storage unit that records information necessary for business procedures in association with trigger conditions ,
A banking business support system comprising a transaction terminal and a control unit connected to the business system,
The control unit is
Based on the product information recorded in the product information storage unit, generating a trend model, using the trend model of the financial product, predicting the product status in the financial product,
Based on the deposit / withdrawal information recorded in the account information storage unit, a customer situation prediction model is generated, the customer situation prediction model is used to predict a customer situation in the customer,
Based on the product status and the customer status, predict trigger conditions for future transactions including the time when the product is expected to be used by the customer ;
Preliminary information necessary for the procedure in the future transaction is recorded in the preliminary information storage unit in association with the trigger condition ,
When the customer visits the store, identify the customer who visited the store and the date and time
For the identified customer, a proposal for a future transaction is output from the advance preparation information storage unit to the transaction terminal according to advance preparation information associated with a trigger condition corresponding to the store visit date and time, and the product status and Checking whether there is a change in the current status of the customer situation, and outputting to the business system a business instruction using the proposed advance preparation information according to the presence / absence of the current status change. system. A customer information storage unit for storing attribute information for each customer;
The control unit calculates the evaluation result of each customer using at least the deposit / withdrawal information recorded in the account information storage unit and the attribute information recorded in the customer information storage unit,
The banking business support system according to claim 1, wherein the advance preparation information is generated based on the evaluation result. The control unit is
Gather hot keywords over the network,
Identify related services corresponding to the keywords,
3. The banking business support system according to claim 1 or 2 , wherein pre-preparation information for the related service is generated. The control unit is
Using the identification image for each customer, generate an identification model for identifying the customer,
From the transaction terminal, obtain an access image of the accessed customer, perform identity authentication using the access image and the identity identification model,
The banking business support system according to any one of claims 1 to 3 , wherein the proposal is output to the transaction terminal based on a result of the personal authentication. An account information storage unit for storing deposit / withdrawal information for each customer account;
A product information storage unit for storing product information of financial products provided by the bank;
For each customer, a pre-preparation information storage unit that records information necessary for business procedures in association with trigger conditions ,
A method for supporting banking operations using a banking support system comprising a transaction terminal and a control unit connected to the business system,
The control unit is
Based on the product information recorded in the product information storage unit, generating a trend model, using the trend model of the financial product, predicting the product status in the financial product,
Based on the deposit / withdrawal information recorded in the account information storage unit, a customer situation prediction model is generated, the customer situation prediction model is used to predict a customer situation in the customer,
Based on the product status and the customer status, predict trigger conditions for future transactions including the time when the product is expected to be used by the customer ;
Preliminary information necessary for the procedure in the future transaction is recorded in the preliminary information storage unit in association with the trigger condition ,
When the customer visits the store, identify the customer who visited the store and the date and time
For the identified customer, a proposal for a future transaction is output from the advance preparation information storage unit to the transaction terminal according to advance preparation information associated with a trigger condition corresponding to the store visit date and time, and the product status and Checking whether there is a change in the current status of the customer situation, and outputting to the business system a business instruction using the proposed advance preparation information according to the presence / absence of the current status change. Method. An account information storage unit for storing deposit / withdrawal information for each customer account;
A product information storage unit for storing product information of financial products provided by the bank;
For each customer, a pre-preparation information storage unit that records information necessary for business procedures in association with trigger conditions ,
A program for supporting banking operations using a banking support system comprising a transaction terminal and a control unit connected to the business system,
The control unit
Based on the product information recorded in the product information storage unit, generating a trend model, using the trend model of the financial product, predicting the product status in the financial product,
Based on the deposit / withdrawal information recorded in the account information storage unit, a customer situation prediction model is generated, the customer situation prediction model is used to predict a customer situation in the customer,
Based on the product status and the customer status, predict trigger conditions for future transactions including the time when the product is expected to be used by the customer ;
Preliminary information necessary for the procedure in the future transaction is recorded in the preliminary information storage unit in association with the trigger condition ,
When the customer visits the store, identify the customer who visited the store and the date and time
For the identified customer, a proposal for a future transaction is output from the advance preparation information storage unit to the transaction terminal according to advance preparation information associated with a trigger condition corresponding to the store visit date and time, and the product status and Checking the presence or absence of the current status change of the customer status, and functioning as a means for outputting a business instruction using the proposed advance preparation information to the business system according to the presence or absence of the current status change Banking support program.

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