Practical skills for credit card auditing of commercial banks

In the past ten years, the credit card business has become the main source of retail business for commercial banks due to its higher marginal rate of return and better asset quality. One of the sources of customers. The following is the knowledge I have brought to you about the practical skills of commercial bank credit card audits. Welcome to read.

1. Audit Sampling

Credit card business sampling is different from other business sampling. In view of the huge volume of credit card issuance and transactions, statistical sampling or experience is simply used. Sampling cannot meet the requirements of audit objectives, so off-site audit sampling combined with experience sampling can be considered. When audit resources are limited, this sampling method can be targeted and improve audit efficiency.

(1) Experience Sampling

Commercial bank credit business sampling generally adopts the following principles: large-amount sampling principle, product coverage principle, priority sampling of non-performing assets, etc., which are generally based on The impact degree, frequency and reverse thinking of risk occurrence, credit card business institution sampling and sample sampling can also refer to the above principles. For example, in terms of institutional sampling, you can choose sub-centers or branches with high incoming volume, card issuance volume, transaction volume, defective rate or amount, or high incidence of counterfeit applications, suspected fraud, or suspected cash-out; in terms of sample sampling, according to the above principles, A sample of institutions to be sampled will be selected for focused sampling.

In addition, reverse thinking can also be used to collect statistics on the non-performing rate and non-performing amount of credit card issuance groups, card issuance channels, and approval projects across the bank. Samples with rates and non-performing amounts higher than the average should be carefully sampled to identify flaws and loopholes in the card-issuing population, card-issuing channels, or the design or execution of approval projects.

(2) Off-site audit sampling

In view of the fact that domestic joint-stock banks have generally established off-site audit systems, some are developed based on the SAS platform such as ICBC and China Construction Bank, and some are based on their own data Warehouse development such as China Merchants, CITIC, etc. The data sources of each bank's credit card business may be slightly different. This article only provides model ideas for off-site audit sampling of credit card business. If the corresponding data fields are defaulted, the audit model may not be applicable. The audit model requirements are as follows:

Card issuance link:

1. The principal credit card applicant is under 18 years old or over 65 years old.

Model idea: Find the account information of the main cardholder who does not meet the card issuance conditions. For example, since January 1, 2015, the data of (interception of card issuance date) card issuance year - (interception of birth date) birth year < 18 or card issuance year - birth year > 65 are extracted.

2. Credit card applicants work in industries such as guarantee, pawn, agency, small loan, etc.

Model idea: Extract the working units of principal credit card holders since January 1, 2015, including enterprises in high-risk fields such as "guarantee", "pawn", "agent", and "small loan" Name word data.

3. The applicant’s workplace is a blacklisted customer of our bank.

Model idea: Extract data from customers whose work unit is UnionPay or bank blacklist since January 1, 2015. Use UnionPay risk management platform data or each bank's blacklist customer data to match the cardholder applicant's work unit.

4. Personal credit record does not meet our bank’s customer access requirements for credit card applications.

Model idea: Find the account information of cardholders whose credit records do not meet our bank’s credit card access requirements. Use the credit reporting system of the People's Bank of China to extract the cardholders' credit card that was overdue for more than 60 days once, or overdue for more than 30 days twice during the 24 months before the approval date since January 1, 2015, or in the 6 months before the approval date. Data that is overdue for more than 30 days at a time[2].

5. Credit card customers have multiple customer numbers.

Model idea: Find customers with higher risk concentration.

Set the ID number as a grouping variable, and the customer number record count is greater than or equal to 2 credit card issuance records.

6. The same cardholder has swiped the card more than 5 times at the same POS merchant, and the cumulative amount of swiped cards exceeds 50,000.

Model idea: Find the transaction details of repeated cash-outs by cardholders at suspicious merchants. Use the cardholder's ID number (one person may hold multiple credit cards) and merchant code to perform grouping and summary, and extract the number of card swipes the same cardholder has made at the same merchant more than 5 times since January 1, 2015, and the total number of card swipes exceeded 5 times. Ten thousand records (the total transaction amount is greater than or equal to 50,000, and the number of transaction records is greater than or equal to 5 times). Online transaction data on e-commerce platforms should be excluded from transaction data such as phone recharge, refueling, etc. such as China Mobile, China Unicom, Telecom, PetroChina, and Sinopec.

7. The same cardholder makes more than 5 card transactions in a single day, and the cumulative amount exceeds 50,000.

Model idea: Find the transaction details of repeated cash-outs by cardholders at suspicious merchants. Use the cardholder's ID number (one person may have multiple cards) and transaction date to perform grouping and summary, and extract records in which the same cardholder has made more than 5 card transactions in a single day since January 1, 2015, and the cumulative amount exceeds 50,000. , arranged in reverse order by cardholder's card number and transaction amount. (The total transaction amount is greater than 50,000 and the number of transaction records is greater than 5 times);

8. The same cardholder’s single credit card payment exceeds 100,000, and the merchant type is business, small loan, guarantee , pawn shops and other high-risk merchants.

Model idea: Extract the cardholder’s abnormal transaction details since January 1, 2015. The above transactions may involve cashing out credit card funds, or the acquiring merchant's merchant code may be applied. The amount of a single transaction is greater than 100,000, and the 8th to 11th digits of the merchant's MCC code are not ?7011?, ?5811?, ?5812?, ?1520?, ?5511?, ?5094?, etc.

Monitoring link:

9. The contact person on multiple credit card application forms is the same person.

Model idea: Extract those who used fraud and counterfeiting to apply for cards since January 1, 2015. Use the grouping summary function to set the applicant's contact phone number as a grouping variable, and the account information record is greater than or equal to 5 records;

10. Multiple cardholders come from the same work unit (cardholder Number of accounts? 20) and the industry is shadow banking related industry.

Model idea: Extract the account information of cardholders belonging to the same company since January 1, 2015, use the work unit to group and summarize, and filter out the number of card holders in the same unit? 20, and the name of the work unit contains Words like "guarantee", "pawn", "agent", "small loan", etc. 11. The same company or personal account transfers and repays to more than 5 non-personal credit cards.

Model idea: Find the cardholder account information whose repayment source is "one-to-many". Use the group summary function to extract credit card repayment records and transfer and repayment transaction details records where the repayer and cardholder account names are inconsistent. Set the payer account number and transaction card number as grouping variables, and the account transaction details record count is greater than or equal to 5 records.

12. There are more than 5 consecutive repayments at the credit card cash counter on the same day, and the operating teller is the same teller.

Model idea: Extract cardholder account information whose repayment source is "one-to-many" cash deposit since January 1, 2015. Extract the credit card cash repayment transaction details record, set the transaction date and transaction teller number as grouping variables, and the teller transaction number as the sorting variable. The sorting order is from small to large; use the teller transaction number of the next record - the teller transaction number of this record =1 records are all listed, and then grouped and summarized again according to date and transaction teller number, and the records with more than 5 records are counted.

13. Multiple cardholders apply for installment business at the same merchant (the cumulative installment amount is 10% of the day's acquiring transaction amount).

Model idea: Extract suspicious merchants that may have handled false installment business since January 1, 2015.

Use the data correlation function to associate the transaction details of the credit card system with the transaction details of the core business system, filter the installment transaction details, group and summarize them, set the merchant code and transaction date as grouping variables, the installment amount and the receipt amount as summary variables, and summarize the day Transactions in which the installment amount is greater than 10% of the acquiring transaction amount.

14. Special merchants with abnormal acquiring transaction volume; (the month’s acquiring transaction volume increased by more than 100% compared with the monthly average acquiring transaction volume).

Model idea: Find special merchants with abnormal changes in acquiring transaction volume. Extract the merchant's acquiring transaction details, summarize the average monthly acquiring transaction volume from January to December 2015, and select merchants whose acquiring transaction volume in that month increased by more than 100% compared with the monthly average acquiring transaction volume;

Model idea: ① A transaction that is similar to the amount of the card swiped within ten days after the card is swiped. The transaction meets the following conditions: first, the transaction amount is greater than 80% of the card swipe amount; second, from the special merchant's corporate or personal account Transaction conditions for transfer back to the cardholder's debit card account; ② For transactions that are similar to the card amount within ten days after the card is swiped, the transaction meets the following conditions: first, the transaction amount is greater than 80% of the card amount, and second, cash is deposited Transaction details of the cardholder's debit card account; ③ Transactions that are similar to the amount of the card swiped within ten days after the card is swiped, and the transaction meets the following conditions: first, the transaction amount is greater than 80% of the card swipe amount, second, from the special merchant enterprise or individual The account is transferred to the employee's debit card account (need to be checked with the human resources system data of each bank).

16. Using third-party payment platforms to cash out.

Model idea: Extract that since January 1, 2015, the transactions of high credit limit cardholders (credit limit of more than 50,000) are mainly concentrated on third-party payment platforms (such as Taobao, Alipay, Tenpay etc.), the transaction time is concentrated within 5 days after the bill date, the cumulative transaction amount reaches more than 90% of the credit limit, and then the repayment time is concentrated in the credit card transaction details 5 days before the final repayment date;

Funds Flow analysis model:

17. Credit card cash flow analysis.

Model idea: Based on the existing credit card cash-out data, extract the capital flow of cash-out credit cards and debit cards in the name of the cardholder, analyze the specific fund flow after cash-out, and focus on credit card cash-out funds. ① Entering the stock market, ② Returning due loans, ③ Entering P2P platforms, ④ Suspected of private lending.

Special reminder that the above model parameters can be adjusted based on the actual business conditions of each industry. Since some commercial banks' credit card business systems are developed independently, credit card transaction data cannot be associated with each bank's core system data, and some audit model ideas may not be implemented.

2. On-site audit skills

Based on my theoretical foundation and many years of internal audit work experience, I gave some suggestions on the on-site audit skills of the credit card business. Please forgive me, experts and scholars.

First, unlike previous audits, the credit card business cannot conduct walk-through testing one by one like general credit business due to the diversification of risks. In order to highlight the key points of the audit, improve the efficiency of the audit, and reflect the overall operating status and risk status of the bank's credit card business, the auditors often adopt the method of seeing the big from the small and drawing inferences from one instance to infer the soundness of the design of the credit card risk control model based on the sampling results. sex, rationality and effectiveness.

Second, use the "reverse investigation method" to start from the analysis of the causes of non-performing assets, evaluate the impact of internal and external factors on asset quality, and focus on the relationship between internal process management and asset quality. For example, evaluate the collection efficiency and effectiveness of the collection process. Collection efficiency: Investigate the "bottleneck links" in the collection process. In terms of collection effectiveness, investigate the degree of collaboration between the card center and branches and the depth and breadth of branch intervention. Is there any further cooperation in customer information sharing and asset preservation? space, and put forward feasible suggestions in a targeted manner.

Third, collect more relevant product information, channel information, market activities and performance indicators from peers. Product information mainly includes product types, product pricing, customer rights and interests, etc. Channel information mainly focuses on understanding channel construction and business Cooperation status and marketing activities mainly include understanding the construction status of the special merchant system and the development of market activities, etc., while performance indicators include understanding the customer base of peers, resource investment intensity, profit structure, asset quality and liquidity management, etc. Evaluate the gaps and shortcomings with peers and formulate specific feasible improvement suggestions. Look for gaps in indicators and conduct in-depth analysis of internal and external problems through the gaps, so that you can get a comprehensive market competitiveness analysis conclusion. ;