How can financial institutions trace and control scams related to "digital renminbi"?

Author: Xiong Runmiao, Southern Metropolis Daily

Recently, a new type of scam under the name "Digital Renminbi" has emerged aggressively. Criminals are taking advantage of the general public's lack of understanding of digital renminbi and their eagerness to participate, using the banner of "Digital Renminbi" to continuously innovate their scams, resulting in financial losses and information leaks for the public. Scammers have utilized the anonymity and loosely coupled payment features of digital renminbi in various locations, attempting to bypass the risk control systems of financial institutions and the investigative techniques of law enforcement, while concealing the flow of funds to evade timely freezes of accounts by regulatory authorities. In the face of this new situation, in addition to increasing anti-fraud education for the public, how should financial institutions, as important participants in digital renminbi, act in this anti-fraud and anti-money laundering campaign?

Responding to Digital Renminbi Fraud: A New Challenge for Bank Security and Risk Control

"To address the new challenges posed by the fraud risks associated with digital renminbi, commercial banks and payment institutions need to upgrade their front-end identity verification and checks for accounts, conduct due diligence on merchant information, and ensure the security certification of transaction terminals. They should also improve monitoring of abnormal fund transactions, especially real-time screening rules and early warning models for abnormal transactions involving digital renminbi, and enhance account and merchant management through various technical means to accurately perceive, dynamically defend, and block suspicious transaction behaviors, severing problematic funding chains." Yu Wei, an industry security expert from Tongdun Technology and an anti-fraud lecturer at the Payment and Clearing Association, suggests that commercial banks and payment institutions should focus on three areas of work.

First, they should pay attention to abnormal funding chains related to digital renminbi and upstream and downstream accounts, strengthen KYC for digital renminbi wallets, and detect suspicious risk transactions related to recharge and withdrawal functions. They should enhance the security certification of co-built apps to prevent and identify risks such as account wallets being stolen, unauthorized transfers, withdrawals, recharges, and copying theft, while also improving the capture of temporal features in business links for real-time analysis and early warning.

Second, they should further enhance monitoring of suspicious fund transactions by establishing an intelligent real-time and near-real-time detection system for abnormal signing, binding cards, recharging, and other money laundering-related fund flows and transaction characteristics involving digital renminbi, effectively improving the precision of risk identification.

Third, they should comprehensively investigate and identify transactions related to digital renminbi and associated counterpart funding accounts, timely severing the payment chains of transaction funds. Given the high frequency of digital currency transactions, multiple accounts, and decentralized trading characteristics, knowledge graph technology can be introduced for tracing analysis, correlation analysis, and case consolidation analysis to further uncover involved criminal groups.

"Fundamentally, financial institutions need to strengthen their real-time control capabilities for abnormal funding chains presented by new risks and characteristics of digital renminbi transactions, from initial customer understanding and risk identification to transaction control, severing fraudulent transaction chains, and post-event risk rating, feeding back into strategy optimization, thereby improving the fraud defense system for digital renminbi transactions," Yu Wei stated.

Strengthening Dual Management of Accounts and Merchants to Enhance Fund Traceability

In Yu Wei's view, financial institutions should build a decision-making system for currency-related characteristics, rules, models, and graph analysis based on effective KYC and trusted systems, as well as monitoring of digital wallet transactions, through data supply from big data platforms and risk control marketplaces. This will ultimately generate a list of digital currency transaction accounts/merchants, severing the transaction chains of involved funds and completing risk disposal and effect analysis to feed back into the optimization of risk control strategies.

Financial institutions also need to focus on monitoring suspicious "receiving accounts" in light of the characteristics of illegal fund transfer transactions. In many problematic transactions, the transferor or payee often changes "dummy" accounts or engages in false recharges and payment transactions to evade bank risk control. Therefore, banks and payment institutions should further establish a risk blacklist for illegal fund transfer transactions and implement a "zero tolerance" approach to illegal transaction accounts.

Many currency-related fraudulent fund transactions are carried out by organized groups with clear upstream and downstream divisions of labor. Financial institutions must be cautious of digital renminbi being used in online gambling, money laundering, and other organized fraudulent crimes. Therefore, it is recommended that financial institutions, while deploying models for accounts or merchants, also utilize knowledge graph technology to conduct in-depth exploration, tracing analysis, and correlation analysis of upstream and downstream accounts involved in group crimes.

In addition to focusing on the connections between digital currency, wallets, and the financial system, and adopting measures such as "cutting payments," financial institutions can also proactively learn and identify unknown abnormal events to sniff out potential currency transaction risk fraud patterns. A terminal security risk awareness system, based on device fingerprint technology and network-side data, combined with big data processing technology, business knowledge accumulation, and AI algorithm models, can actively lock onto new risk sources that threaten existing systems and information, achieving an overall closed-loop chain of proactive awareness, detection, defense, visualization, linkage, and tracing of network-wide risks.