KR20200112622A - Regtech platform apparatus for digital compliance and risk management, method for risk management of financial transactions and computer program for the same - Google Patents

Abstract

A regtech platform apparatus of the present invention comprises: a transmitting and receiving module linked with a financial company executing a financial transaction to receive client information related to a fintech service provided by a fintech provider to a client, and constructed to receive a transaction request including transaction information for a financial transaction from the client or the fintech provider; and at least one analysis module which determines at least one client risk index or at least one transaction risk index from the client information and the transaction information, and is constructed to determine the risk degree with respect to the transaction request on the basis of a scenario based on the client risk index or the transaction risk index. Therefore, according to the regtech platform apparatus, risks expected in financial services and businesses provided by the affiliation of a financial company and a fintech company can be monitored and responded to in advance. Consequently, preemptive response can be performed with respect to changes in the behavior of finance users and the regulation method of the government authorities, and measures can be taken in advance against expected risks, thereby guaranteeing the permanence of services or businesses.

Description

Legtech platform device for digital compliance and risk management, financial transaction risk management method, and computer program therefor {REGTECH PLATFORM APPARATUS FOR DIGITAL COMPLIANCE AND RISK MANAGEMENT, METHOD FOR RISK MANAGEMENT OF FINANCIAL TRANSACTIONS AND COMPUTER PROGRAM FOR THE SAME}

The embodiments relate to a regtech platform device, a financial transaction risk management method, and a computer program therefor. In more detail, the embodiments are related to a technology for monitoring and proactively responding to risks expected in financial services and businesses provided by a financial company and a fintech company in partnership.

The financial industry is a foundational industry of society, and financial services are required to be regulated by the government for stability of the national economy and protection of financial consumers. However, as fintech, a new technology that combines finance and technology, has recently attracted attention, the area of financial service providers has become information & communication technology (ICT) in traditional financial companies. ), etc., are expanding to non-financial companies.

In line with these changes, the government's regulation of financial services has also changed from the positive system of the past to the negative system, and the risk management responsibility of financial service providers is strengthening. In the past enumerationist method of regulation, risk factors were predictable because only permitted tasks were applied, so it was necessary to apply a generalized risk management method to the financial industry. However, while the regulatory method changed to a comprehensive method, new business opportunities were expanded. It is difficult to predict risk factors for new financial services.

For example, risk management of existing financial companies includes credit ratings for borrowers, real name verification and customer verification system (Know Your Customer (KYC)) to verify personal transactions, and verification of the purpose and source of funds for financial transactions. Just follow the risk management system presented in. As an example of such a conventional technology, Korean Patent Publication No. 10-1717596 discloses a risk management technology that detects abnormal transactions based on user account information.

However, the realization of fintech services in a situation where the customer's use of financial services such as peer-to-peer (P2P) loans, simple payments, and remittances (including overseas remittances) is changing to fintech services provided by non-financial companies. For financial purposes, financial transactions requested from non-financial companies and processed by financial companies have limitations that cannot be managed with conventional risk management systems.

Registered Patent Publication No. 10-1717596

According to an aspect of the present invention, digital compliance and risk by proactively responding to the changing behavior of financial users and regulatory methods of governmental authorities in accordance with the changing reality of financial service use channels and transaction types. It is possible to provide a regtech platform device capable of performing risk management, a financial transaction risk management method, and a computer program for the same.

The regtech platform device according to an embodiment receives customer information related to a fintech service provided to a customer by a fintech service provider in connection with a financial company executing a financial transaction, and the customer or A transmission/reception module configured to receive a transaction request including transaction information for financial transaction from the fintech provider; And one configured to determine one or more customer risk indicators or one or more transaction risk indicators from the customer information and the transaction information, and determine a risk level for the transaction request based on the customer risk indicator or a scenario based on the transaction risk indicator. It includes the above analysis module.

In one embodiment, the one or more analysis modules include: a customer risk analysis module, configured to determine the one or more customer risk indicators from the customer information; A transaction risk analysis module configured to determine the one or more transaction risk indicators from the transaction information; And a risk assessment management module configured to determine a risk level for the transaction request by using one or more of the one or more customer risk indicators and the one or more transaction risk indicators.

In one embodiment, the risk assessment management module is further configured to generate the scenario for evaluating the risk of financial transactions by clustering transaction cases using at least one of the customer risk indicator and the transaction risk indicator.

In one embodiment, the risk assessment management module generates a normal group group in which normal transaction cases are clustered using at least one of the customer risk index and the transaction risk index, and the transaction request belonging to a group different from the normal group group Is configured to generate the above scenario to determine the risk target.

In one embodiment, the one or more analysis modules are configured to generate a report corresponding to the transaction request in which the risk determined based on the scenario is equal to or greater than a preset threshold and transmit it to the customer or the financial transaction supervisory authority. It further includes a module.

In one embodiment, the customer information is among the customer classification, gender, age, occupation, contact information, address, recent transaction date and time, recent transaction channel, recent transaction location, balance, number of financial products and number of transaction accounts. Contains information about one or more.

In one embodiment, the transaction information includes information on one or more of a transaction name, transaction date, transaction type, transaction channel, transaction amount, counterparty, whether or not the transaction is normally processed, transaction account number, transaction partner account number, and transaction location. Includes.

A financial transaction risk management method according to an embodiment includes the steps of: receiving, by a legtech platform device, customer information related to a fintech service provided to a customer by a fintech business operator in connection with a financial company executing a financial transaction; Receiving, by the legtech platform device, a transaction request including transaction information for financial transaction from the customer or the fintech operator; Determining, by the legtech platform device, at least one customer risk index or at least one transaction risk index from the customer information and the transaction information; And determining, by the Legtech platform device, a risk level for the transaction request based on the customer risk indicator or a scenario based on the transaction risk indicator.

In the financial transaction risk management method according to an embodiment, the Legtech platform device generates the scenario for evaluating the risk of financial transactions by clustering transaction cases using one or more of the customer risk indicator and the transaction risk indicator. It further includes the step of.

In one embodiment, the generating of the scenario comprises: generating, by the Legtech platform device, a normal group group in which normal transaction cases are clustered using at least one of the customer risk index and the transaction risk index; And generating the scenario so that the legtech platform device determines the transaction request belonging to a group different from the normal group group as a risk target.

The financial transaction risk management method according to an embodiment further comprises generating, by the Legtech platform device, a report corresponding to the transaction request in which a risk determined based on the scenario is equal to or greater than a preset threshold.

The financial transaction risk management method according to an embodiment further includes transmitting, by the Legtech platform device, the report to one or more of the customer and the financial transaction supervisory authority.

The computer program according to an embodiment is for executing the financial transaction risk management method according to the above-described embodiments by being combined with hardware, and may be stored in a computer-readable recording medium.

According to the regtech platform device and the financial transaction risk management method according to an aspect of the present invention, (1) a risk assessment methodology based on a risk index that combines customer behavior and financial transaction type, (2) big data technology Risk analysis and financial transaction risk analysis through customer use behavior, (3) risk prediction model through scenario-based simulation, and (4) automatic creation of scenarios incorporating artificial intelligence technology and automated risk prediction through simulation By doing so, it is about technology that monitors and responds to risks expected in financial services and businesses provided in partnership with financial companies and fintech companies.

Using the above legtech platform device and financial transaction risk management method, (i) a risk management system for firm banking and virtual accounts provided to fintech companies can be prepared, and an integrated regulatory system can be established. , (ii) Easy remittance and payment, P2P (peer-to-peer) loans, cryptocurrency exchanges, etc., develop a risk prediction model for each area of fintech services, and develop a risk management model for safe fintech partnership business. , (iii) It is possible to establish a monitoring list, such as a database of customers of interest within financial companies.

According to the above features, according to the Legtech platform device and the financial transaction risk management method according to an aspect of the present invention, digital compliance is preemptively responding to changing behaviors of financial users and regulatory methods of government authorities. And it is possible to perform risk management, and as a result, there is an advantage of ensuring the continuity of the service or business by taking preliminary measures against the expected risks.

1 is a conceptual diagram illustrating a service structure associated with a regtech platform device according to an embodiment.
2 is a schematic block diagram of a legtech platform device according to an embodiment.
3 is a flowchart of a financial transaction risk management method according to an embodiment.
4 is a conceptual diagram illustrating management of a risk index by a method for managing financial transaction risk according to an exemplary embodiment.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings.

1 is a conceptual diagram illustrating a service structure associated with a regtech platform device according to an embodiment.

Legtech is a compound word of regulation, which means regulation, and technology, which means technology, and refers to information technology that facilitates internal control and compliance by financial companies. In this specification, Legtech refers to a technology for the purpose of digital compliance, which is regulatory compliance for digital data, and information risk management through it, and the embodiments are led by financial companies in order to realize such Legtech technology. The present invention relates to a usable platform device, a risk management method for financial transactions related thereto, and a computer program for performing the method.

Referring to FIG. 1, embodiments of the present invention are related to financial transactions through a fintech business operator providing financial services in connection with a financial company such as a bank. The user (1) uses the fintech service provided by the fintech service provider through the fintech service provider server (2), and the actual financial transaction for the implementation of the fintech service is conducted by the financial company server (3) affiliated with the fintech service provider. ), and the financial company server 3 performs settlement of funds according to financial transaction results in real time or periodically based on the account of the fintech business operator.

In this specification, the fintech business is not a financial company, but in partnership with a financial company, simple remittance, simple payment, peer-to-peer (P2P) loans, cryptocurrency trading, or other financial transactions It refers to a business operator that provides a service in any form, and is not limited to a business operator related to a specific type of business or service.

The financial company server 3 receives and processes a financial transaction request through a non-face-to-face channel made through a fintech business, and a transaction management unit 31, and firm banking and virtualization necessary for realization of fintech services. Including the account management unit 32 that performs account issuance management, etc., the financial transaction of the user 1 is made possible through an affiliated institution called a fintech business operator.

In one embodiment, the financial company server 3 is an API providing unit that provides an API (Application Programming Interface) for requesting a financial transaction from a user device related to a fintech service or a fintech service provider server to the financial company server 3 (33) may be included.

In the conventional financial company server 3, risk management for financial transactions was performed using a money laundering prevention unit 34 or a financial fraud management unit 35. The money laundering prevention department (34) communicates with the Korea Financial Intelligence Unit (KFIU) server (4) in accordance with the Act on Reporting and Use of Specific Financial Transaction Information, and 　Know Your Customer (KYC). ), the Suspicious Transaction Report (STR) and the Currency Transaction Report (CTR), thereby realizing Anti-Money Laundering (AMR). In addition, the financial fraud management unit 35 communicates with the Financial Supervisory Service server 5 in accordance with the Special Act on Telecommunications Financial Fraud Damage Prevention and Damage Refund, and is a part for reporting suspicious fraud transactions or stopping payment.

However, the risk management by the money laundering prevention unit 34 and the financial fraud management unit 35 is implemented around the business of financial companies' branch offices, so the current situation where customer contact channels are expanding through affiliated organizations such as fintech companies. There was a problem that it was difficult to flexibly respond to various types of transactions. In addition, risk management by the money laundering prevention department (34) and financial fraud management department (35) is that the subject of determining whether or not a suspicious transaction is an employee of a financial company's branch office, so there is always a possibility that the transaction may be exposed to risk due to human error or carelessness There is a limit.

In addition, the risk management method in the Financial Information Analysis Institute server 4 and the Financial Supervisory Service server 5 is built around internet banking or smart banking, so the limitation of relying on the collection of information from the user device used by the user 1 In addition, there is a limitation in that a comprehensive management system does not exist because risk management for firm banking and virtual accounts in financial companies is carried out on a case-by-case basis according to the administrative guidance of the supervisory authority. In particular, fintech service providers' services are often based on firm banking and virtual accounts.However, there is no system for realizing compliance or performing risk management in response to related regulations, so non-face-to-face channels It was a reality that the financial company's branch staff were relied on to determine whether or not a financial transaction was a suspicious transaction.

The present inventors have derived a legtech platform device 30 in order to solve the problems of the prior art as described above. In the embodiment shown in FIG. 1, the legtech platform device 30 is described as being implemented as a part of the financial company server 3 and operated by the financial company. However, this is exemplary, and in another embodiment, the legtech platform device 30 may be implemented as a separate system from the financial company server 3 configured to provide information necessary for risk management at the request of the financial company server 3. May be.

2 is a schematic block diagram of a legtech platform device according to an embodiment. For convenience of explanation, a legtech platform device according to this embodiment will be described with reference to FIGS. 1 and 2.

Referring to FIG. 2, the legtech platform device 30 according to an embodiment includes a transmission/reception module 300 and one or more analysis modules 321-324. For example, in one embodiment, one or more analysis modules include a customer risk analysis module 321, a transaction risk analysis module 322, a risk assessment management module 323, and a monitoring module 324, or at least one of them. It may include. In addition, in an embodiment, the legtech platform device 30 further includes an information processing module 310 that performs data operation for implementing the functions of the analysis modules 321-324 described above.

The systems, devices, and servers described herein may have aspects that are entirely hardware, or partially hardware and partially software. For example, each module or unit included in the legtech platform device according to the embodiments is hardware for processing data of a specific format and content or/or sending and receiving data in an electronic communication method, and software related thereto. Can be collectively called. In this specification, terms such as "unit", "module", "device", "terminal", "server" or "system" are intended to refer to a combination of hardware and software driven by the hardware. For example, the hardware may be a data processing device including a CPU or other processor. In addition, software driven by hardware may refer to an executing process, an object, an executable file, a thread of execution, a program, and the like.

Each element constituting the legtech platform device 30 according to the present embodiment is not necessarily intended to refer to separate devices that are physically separated from each other. That is, each of the modules 300, 310, 321-324 shown in FIG. 2 is only functionally divided hardware constituting the legtech platform device 30 according to an operation performed by the corresponding hardware, and must be each It does not have to be provided independently of each other. Of course, depending on the embodiment, one or more of the modules 300, 310, 321-324 shown in FIG. 2 may be implemented as separate devices that are physically separated from each other.

The transmission/reception module 300 is not a financial company that directly executes financial transactions, but receives customer information related to a fintech service of a fintech service provider providing a service in connection with the financial company server 3. In addition, the transmission/reception module 300 receives a transaction request including transaction information for a financial transaction related to a corresponding fintech service. The transmission/reception module 300 may receive customer information and transaction requests from the fintech service provider server 2, or use an application (or app) provided to the fintech service provider. At least a part of customer information and transaction requests may be received from a user device (eg, a smartphone, etc.) of the user 1 who wants to use the service.

The communication process between the transmission/reception module 300 and the user device or the fintech operator server 2 is performed through a wired and/or wireless network. The communication method through a wired and/or wireless network may include all communication methods capable of networking an object and an object, and is not limited to wired communication, wireless communication, 3G, 4G, or other methods. For example, wired and/or wireless networks include Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), and High Speed Downlink Packet Access (HSDPA). , W-CDMA (Wideband Code Division Multiple Access), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), Bluetooth, Zigbee, Wi-Fi, VoIP (Voice) over Internet Protocol), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and refers to a communication network by one or more communication methods selected from the group consisting of ultrasonic communication. However, it is not limited thereto.

The information processing module 310 performs data calculation for determination of indicators, scenario generation, and simulation by each analysis module 321-324. In one embodiment, the information processing module 310 is a rule-based engine 311 that stores control logic and business processing rules corresponding to a scenario, and performs machine learning-based learning on customer information and transaction information. An intelligent framework (312) and a process automation unit (313) that provides robot process automation (RPA) and artificial intelligence (AI) tools for creating scenarios based on indicators. Includes.

Each of the analysis modules 321-324 determines one or more customer risk indicators and/or one or more transaction risk indicators from customer information and/or transaction information received by the transmission/reception module 300, and creates a scenario based on the determined indicator. It is configured to determine the level of risk for the transaction request based on it. That is, each analysis module (321-324) determines an index for financial transaction risk assessment through machine learning realized by the information processing module 310, or selects a risk target transaction based on the risk index data, It is possible to perform operations such as calculating a risk score by assigning weights for each risk indicator.

The customer risk analysis module 321 stores and stores information on a user related to a financial transaction to evaluate risk, and stores account registration information and account issuance details such as the registration date for fintech service and the number and type of registered services. It is a part to derive customer risk indicators based on it. At this time, the customer-related information may be linked with the financial company customer information stored in the existing legacy system of the financial company. In addition, the customer risk analysis module 321 may manage a watch list of customers corresponding to potential risk customers based on the risk analysis result.

The transaction risk analysis module 322 converts transaction-related information, such as user's deposit and withdrawal transaction details through fintech service, and fintech business' deposit and withdrawal transaction details, into a database, and based on this, the deposit and withdrawal transaction date, amount, and amount of each fintech service This is a part to derive transaction risk indicators through pattern analysis on the number of times. In addition, the transaction risk analysis module 322 may be linked with a legacy system of a financial company in order to obtain transaction information.

The risk assessment management module 323 generates a risk scenario based on the evaluation indicators derived by the customer risk analysis module 321 and the transaction risk analysis module 322, and generates risk through risk assessment simulation through the generated scenario. This is for developing a predictive model. This will be described in detail later with reference to FIG. 3.

The monitoring module 324 is a part for reporting a risk customer and/or a risk transaction corresponding to the risk prediction model determined by the risk assessment management module 323 to a user and/or a supervisory authority. In addition, the monitoring module 324 may perform a report according to STR and CTR through communication with the Financial Information Analysis Institute server 4. Reports on risky customers, risky transactions, and abnormal transaction patterns generated by the monitoring module 324 may be used to check the validity of a transaction purpose and investigate whether a financial accident has occurred.

The detailed risk indicator determination and risk assessment process by each of the customer risk analysis module 321, the transaction risk analysis module 322, the risk assessment management module 323, and the monitoring module 324 will be described in detail with reference to FIG. 3 do.

3 is a flowchart of a financial transaction risk management method according to an embodiment. For convenience of explanation, a financial transaction risk management method according to the present embodiment will be described with reference to FIGS. 1 and 3.

First, the transmission/reception module 300 of the legtech platform device 30 may receive customer information related to the fintech service from the user device of the user 1 using the fintech service or the fintech service server 2 ( S1). At this time, customer information refers to the customer's profile (customer classification, gender, age, occupation, contact information, address, etc.), recent transaction date and time, recent transaction channel, recent transaction location, balance, number of financial products and/or number of transaction accounts. May include information about. At this time, the customer classification may be information indicating characteristics of a customer, such as an individual, a corporation, an individual business entity, or an arbitrary organization.

The above customer information may be transmitted entirely from an external device to the transmission/reception module 300, or by referring to customer information of a financial company customer stored in the existing legacy system of the financial company server 3, among them, fintech service It may be transmitted to the transmission/reception module 300 in a manner that specifies a user.

In addition, the transmission/reception module 300 may receive a transaction request for execution of financial transactions accompanying fintech services such as simple remittance, simple payment, P2P loan, cryptocurrency trading, etc. from the user device or the fintech business server 2 Yes (S2). At this time, the transaction information includes information on the transaction name (product or service, etc.), transaction date, transaction type, transaction channel, transaction amount, counterparty, whether or not the transaction is normally processed, transaction account number, counterparty account number and/or transaction location. May be included. In addition, the transaction type at this time means new account, account inquiry, account transfer, general deposit, general withdrawal, automatic transfer registration, automatic debit withdrawal, payment transfer deposit, virtual account registration, virtual account deposit, firm banking deposit, firm banking automatic transfer, Information that can specify the nature of the product or service related to the financial transaction, such as firm banking payment transfer, Giro automatic transfer, deposit new, deposit cancellation, foreign currency remittance (start), foreign currency remittance (external), loan application, and loan termination Means.

At this time, the analysis modules 321-324 of the legtech platform device 30 may select a risk index from among various pieces of information included in customer information and transaction information (S3). For example, determining one or more customer risk indicators from customer information may be performed by the customer risk analysis module 321, and determining one or more transaction risk indicators from transaction information is performed by the transaction risk analysis module 322 Can be.

Next, the risk assessment management module 323 among the analysis modules of the legtech platform device 30 uses the indicators determined by the customer risk analysis module 321 and the transaction risk analysis module 322 to reduce risks such as fraud or falsehood. A risk assessment scenario is generated to select a transaction request suspected of being a transaction (S4), and a customer risk analysis (S5-1) and/or a transaction risk analysis (S5-2) are performed based on the generated scenario. This can be performed by comprehensively applying rule-based management and artificial intelligence-based big data analysis provided by the information processing module 310.

4 is a conceptual diagram illustrating management of a risk index by a method for managing financial transaction risk according to an exemplary embodiment.

4, in the risk assessment management module 323, among customer information 3001, customer classification, gender, age, occupation, contact information, address, number of transaction accounts, average balance, number of financial products, recent transaction date, and recent Information determined by customer risk indicators such as transaction location information and recent transaction channel, transaction name, transaction date, transaction type, transaction channel, transaction amount, counterparty information, transaction processing status, transaction account number among transaction information (3002) , Based on information determined by transaction risk indicators such as counterparty account number and transaction location information, it is possible to evaluate whether a product or service defined by a specific transaction request is suspected of being a risky transaction.

As a specific evaluation process, the risk assessment management module 323 performs cluster analysis in which a number of transaction requests are grouped into one or more clusters based on each risk indicator data, and a qualitative group group based on a known normal transaction. Can be created. Next, the risk assessment management module 323 may generate a scenario to determine a risk target when a transaction request belonging to a group different from the generated normal group occurs. For example, if a specific transaction request has characteristics contrary to that of the normal group, the transaction request is likely to be a risky transaction.

Cluster analysis may be performed on a coordinate system in which values corresponding to one or more indicators are used as coordinate values. For example, when age is used as a customer risk indicator and transaction amount is used as a transaction risk indicator, the transaction request is equivalent to (age, transaction amount) on a coordinate system with age as X axis and transaction amount as Y axis. It could be expressed in (x, y) coordinates. The coordinate values of each axis may be scaled in consideration of a relative size with the coordinate values of other axes. At this time, the risk assessment management module 323 performs cluster analysis on points (ie, transaction requests) on the coordinate system corresponding to (age, transaction amount), and a specific transaction request is a risky transaction through comparison with the normal group. Whether or not it can be evaluated.

In the above example, the customer risk index is used as the X-axis and the transaction risk index is used as the Y-axis, but this is only an example, and in other embodiments, a coordinate system may be configured using only the customer risk index or the transaction risk index. have. In addition, it is also possible to perform cluster analysis on a coordinate system composed of three or more dimensions, using three or more indexes.

Scenarios created by the above process are evaluated as risky transactions when three or more accounts are registered for the purpose of automatic transfer to the Fintech service within 3 days after opening the account, or created a virtual account on a P2P loan or cryptocurrency exchange. It refers to a set of rules that can be executed by a rule-based engine, such as evaluating as a risky transaction if a transaction occurs after deposit and withdrawal within 2 days afterwards, to select a transaction request that matches the scenario among transaction requests as risky transactions. I can.

The types of customer risk indicators and/or transaction risk indicators used to create a scenario and the type of transaction to which the scenario is applied may differ depending on the purpose and target of the scenario. For example, Table 1 below summarizes the indicators and target transactions used in the scenario for predicting the risk that the user's account is used as a cannon passbook.As shown in the table, non-face-to-face account new, general deposit, For transaction types such as issuance of virtual accounts and deposits to virtual accounts, in addition to customer information, information related to transfer and deposit was used as a transaction risk indicator.

Scenario target Risk indicator Customer risk indicator Transaction risk indicator New non-face-to-face account Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/Service (automatic transfer registration, automatic transfer withdrawal, payment transfer deposit, general deposit, account transfer), transaction date, transaction channel, transaction amount General deposit Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (account inquiry, withdrawal), transaction channel, transaction date, transaction amount Virtual account issuance Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (account new, account transfer, payment transfer deposit), transaction channel, transaction date, transaction amount Virtual account deposit Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (account new, account transfer), payment transfer deposit (transaction channel, transaction date, transaction amount)

As another example, Table 2 below summarizes the indicators and target transactions used in the scenario for predicting the risk of fraudulent transactions due to voice phishing, and as shown in the table, loan application, virtual account deposit, account transfer, automatic In addition to customer information for transaction types such as transfer and withdrawal, information such as loan application, deposit cancellation, and account transfer was used as a transaction risk indicator.

Scenario target Risk indicator Customer risk indicator Transaction risk indicator Loan application Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (virtual account deposit, deposit new, account transfer), transaction date, transaction channel, transaction amount Virtual account deposit Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (loan application, deposit cancellation, account transfer), transaction channel, transaction date, transaction amount Bank Transfer Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (loan application, deposit cancellation), transaction channel, transaction date, transaction amount Withdrawal Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (loan application, deposit cancellation), transaction channel, transaction date, transaction amount

As another example, Table 3 below summarizes the indicators and target transactions used when evaluating the risk of simple remittance transactions.As shown in the table, automatic transfer registration, automatic debit withdrawal, general deposit, virtual account issuance, etc. In addition to customer information for transaction types, information related to account registration and inquiry, among other transactions, was used as a transaction risk indicator.

Scenario target Risk indicator Customer risk indicator Transaction risk indicator Automatic debit registration Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (automatic transfer registration, deposit cancellation), transaction channel, transaction date, transaction amount Withdrawal Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (loan application, deposit cancellation), transaction channel, transaction date, transaction amount General deposit Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (account inquiry, withdrawal), transaction channel, transaction date, transaction amount Virtual account issuance Age, latest transaction date, average balance, number of financial products held, number of transaction accounts held Product/service (account new, account transfer, payment transfer deposit), transaction channel, transaction date, transaction amount

However, the scenario targets shown in Tables 1 to 3 and the types of indicators accordingly are only exemplary. In the legtech platform device 30 according to the embodiments, the customer risk analysis module 321 and the transaction risk analysis module 322 are known in advance through analysis of fintech services or transaction pattern analysis for each fintech service. It is possible to determine the customer risk indicator or the transaction risk indicator that appears at a high rate in, and the risk assessment management module 323 creates a scenario for selecting high-risk transaction requests by clustering transaction requests based on the determined indicators. I can.

In one embodiment, the risk assessment management module 323 calculates a risk score for each transaction request by assigning weights for each risk index in addition to selecting a risk target through clustering, and determining the final risk level for the transaction request through this. have.

For example, the indicators may appear relatively high in a particular risk trading A _1, A _2, assuming that the A _3, the risk assessment, the management module 323 to be proportional to the priority or frequency of the indicators shown at risk trading A _1, Weights a ₁ , a ₂ , and a ₃ can be assigned to each indicator of A ₂ and A ₃ . At this time, in creating a scenario for selecting risky transactions, in response to a specific transaction request, the risk according to the clustering result using the index A ₁ is determined as b ₁ , the risk according to the clustering result using the index A ₂ is calculated as b ₂ , and the index A _When the risk according to the clustering result using ₃ is b ₃ , the final risk X of the corresponding transaction request may be determined as a value obtained by summing the product of the weight for each indicator and the risk according to the clustering result, as shown in Equation 1 below.

[Equation 1]

X = a ₁ b ₁ + a ₂ b ₂ + a ₃ b ₃

At this time, each of b ₁ , b _2, and b ₃ may be a binary value indicating whether the corresponding transaction request belongs to a cluster to which a normal transaction belongs as a result of clustering for a specific transaction request. For example, if it belongs to the normal group, the b _x value is set to 0, and if it belongs to the group that does not belong to the normal group or has the opposite characteristics, the value of b _{x is} set to 1, so that the risk X is high in a transaction request not belonging to the normal group. Can be produced.

As another example, each of b ₁ , b ₂ and b ₃ represents the distance from the center of the cluster to which the transaction request belongs to the center of the normal group as a result of clustering for a specific transaction request in a coordinate system around one or more indicators. It may be a value, or a value indicating a distance from a point on a coordinate system where a specific transaction request is located to the center of a normal population. In this case, the risk level can be calculated more quantitatively than the case where the risk trading status is expressed as a binary value.

In the example described above, the calculation of the degree of risk has been described using the case of using three indicators as an example, but it is easy for a person skilled in the art that the same principle can be extended for the case of using fewer or more indicators. Will make sense.

Referring back to FIGS. 1 and 3, in one embodiment, the risk assessment management module 323 may perform a simulation for a risk assessment model by comparing the risk assessment result using the generated risk assessment model with past risk cases. Yes (S6). This is a process for determining the risk assessment model to be applied in practice. By calculating the risk assessment scoring compared to past risk cases, the fit of the risk assessment model can be determined.

When the risk assessment model to be applied is determined, the risk assessment management module 323 may determine a risk level for an actual transaction request based on this (S7). In one embodiment, the monitoring module 324 of the legtech platform device 30 generates a report on a risk customer and/or a risk transaction (S8), and notifies the user device of a user associated with the transaction, or / In addition, the effectiveness of the risk assessment may be further increased by reporting in connection with the reporting system of the supervisory authority such as the Financial Information Analysis Institute server 4 and the Financial Supervisory Service server 5 (S9).

According to the Legtech platform device and the financial transaction risk management method according to the embodiments described above, (1) a risk assessment methodology based on a risk index that combines customer behavior and financial transaction type, and (2) a customer through big data technology. By applying risk analysis and financial transaction risk analysis on usage behavior, (3) risk prediction model through scenario-based simulation, and (4) automatic scenario generation and simulation through artificial intelligence technology, Service continuity can be guaranteed by predicting and responding to risks arising from financial transactions through affiliated institutions such as fintech services, a form of financial service.

In addition, the Legtech platform device and the financial transaction risk management method according to the embodiments can automate risk prediction through the creation and simulation of a risk assessment model, can be extended and applied to new types of financial transactions, and can be applied to customer risk and transaction. It is possible to use an evaluation model that combines risks, so that the accuracy of risk prediction is high, and furthermore, the overall regulation of governmental authorities such as the Act on Reporting and Use of Specific Financial Transaction Information, Electronic Financial Transactions Act, Real Name Financial Transactions and Confidentiality Act, etc. It can be usefully used in realizing digital compliance by responding preemptively.

The operation of the financial transaction risk management method according to the embodiments described above may be implemented at least partially as a computer program and recorded in a computer-readable recording medium. A computer-readable recording medium in which a program for implementing an operation according to the financial transaction risk management method according to the embodiments is recorded and includes all types of recording devices in which data that can be read by a computer is stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tapes, floppy disks, and optical data storage devices. In addition, the computer-readable recording medium may be distributed over a computer system connected through a network, and computer-readable codes may be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present embodiment may be easily understood by those skilled in the art to which the present embodiment belongs.

The present invention described above has been described with reference to the embodiments shown in the drawings, but these are merely exemplary, and those of ordinary skill in the art will understand that various modifications and variations of the embodiments are possible therefrom. However, such modifications should be considered to be within the technical protection scope of the present invention. Therefore, the true technical scope of the present invention should be determined by the technical spirit of the appended claims.

Claims (13)

Receive customer information related to fintech services provided to customers by a fintech business operator in connection with a financial company executing financial transactions, and receive a transaction request including transaction information for financial transactions from the customer or the fintech business operator A transmission/reception module configured to; And
At least one configured to determine one or more customer risk indicators or one or more transaction risk indicators from the customer information and the transaction information, and determine a risk for the transaction request based on the customer risk indicator or a scenario based on the transaction risk indicator Legtech platform device including analysis module.
The method of claim 1,
The one or more analysis modules,
A customer risk analysis module configured to determine the one or more customer risk indicators from the customer information;
A transaction risk analysis module configured to determine the one or more transaction risk indicators from the transaction information; And
Legtech platform apparatus comprising a risk assessment management module configured to determine a risk level for the transaction request using at least one of the at least one customer risk index and the at least one transaction risk index.
The method of claim 2,
The risk assessment management module is further configured to generate the scenario for evaluating the risk of financial transactions by clustering transaction cases using at least one of the customer risk index and the transaction risk index.
The method of claim 3,
The risk assessment management module creates a normal group group in which normal transaction cases are clustered using one or more of the customer risk index and the transaction risk index, and determines the transaction request belonging to a group different from the normal group as a risk target. Legtech platform device configured to generate the scenario to be performed.
The method of claim 2,
The one or more analysis modules,
The legtech platform apparatus further comprises a monitoring module configured to generate a report corresponding to the transaction request having a risk determined based on the scenario equal to or greater than a preset threshold and transmit it to one or more of the customer or the financial transaction supervisory authority.
The method of claim 1,
The customer information includes information on at least one of the customer's customer classification, gender, age, occupation, contact information, address, date of recent transaction, recent transaction channel, recent transaction location, balance, number of financial products and number of transaction accounts. Legtech platform device comprising a.
The method of claim 1,
The transaction information includes information on one or more of a transaction name, transaction date, transaction type, transaction channel, transaction amount, counterparty, whether or not the transaction is normally processed, transaction account number, counterparty account number, and transaction location. Platform device.
Receiving, by the legtech platform device, customer information related to a fintech service provided to a customer by a fintech operator in connection with a financial company executing a financial transaction;
Receiving, by the legtech platform device, a transaction request including transaction information for financial transaction from the customer or the fintech operator;
Determining, by the legtech platform device, at least one customer risk index or at least one transaction risk index from the customer information and the transaction information; And
And determining, by the Legtech platform device, a risk level for the transaction request based on the customer risk indicator or a scenario based on the transaction risk indicator.
The method of claim 8,
The financial transaction risk management method further comprising the step of generating, by the Legtech platform device, the scenario for evaluating the risk of financial transactions by clustering transaction cases using at least one of the customer risk indicator and the transaction risk indicator.
The method of claim 9,
The step of generating the scenario,
Generating, by the Legtech platform device, a normal group group in which normal transaction cases are clustered using at least one of the customer risk index and the transaction risk index; And
And generating the scenario so that the legtech platform device determines the transaction request belonging to a group different from the normal group group as a risk target.
The method of claim 8,
And generating, by the Legtech platform device, a report corresponding to the transaction request in which a risk determined based on the scenario is equal to or greater than a preset threshold.
The method of claim 11,
And transmitting, by the legtech platform device, the report to at least one of the customer and the financial transaction supervisory authority.
A computer program stored in a computer-readable medium to execute the financial transaction risk management method according to any one of claims 8 to 12 in combination with hardware.

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