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RISK DETECTION AND MITIGATION IN FINTECH



Fintech, or financial technology, includes innovative solutions to revolutionize various facets of the financial services industry. These solutions cater to retail customers, corporate entities, and asset management needs. These companies offer digital banking services, payment solutions, and personal finance management tools in retail banking. For corporate clients, fintech solutions include treasury management, working capital optimization, and trade finance platforms. Asset and wealth management offerings encompass robot-advisory services, digital wealth platforms, and access to alternative investments. Across these domains, fintech is pivotal in enhancing accessibility, efficiency, and convenience in financial services. In the rapidly evolving field of fintech, ensuring security and integrity is paramount. These companies face many risks, from fraudulent transactions to market manipulation, necessitating robust solutions to protect financial systems. The following are strategies companies employ to effectively detect and mitigate risks within the company.


Strategies for Risk Detection and Mitigation:


Transaction Monitoring: To safeguard against fraudulent transactions, companies implement robust transaction monitoring systems. These systems track and analyze all transactions in real-time, flagging any unusual or suspicious activities. Transaction monitoring tools are essential for businesses and financial institutions to detect and prevent fraudulent activities, money laundering, and other financial crimes. For example, if a customer suddenly withdraws from their account in a foreign country where they've never conducted transactions, it raises a red flag for further investigation. The most commonly used Transaction Monitors are Verafin, Youverify, Actmize, SAS, etc.


Corporate Registry and Influence Mapping: Maintaining a corporate registry helps identify influential individuals who may pose risks due to their access to sensitive information or market influence. By mapping relationships between individuals and organizations, firms can assess potential risks more effectively. For instance, if a board member of a publicly traded company with a history of regulatory violations is associated with a client account, it warrants closer scrutiny to ensure compliance with regulations.


Collaboration with Third-Party Risk Intelligence Providers: Collaborates with reputable third-party sources like World Check and Refinitiv to access databases of Politically Exposed Persons (PEPs) and heightened-risk individuals. These databases serve as watchlists to identify and manage financial, regulatory, and reputational risks associated with clients and counterparties. For example, if a potential client's name appears on a PEP list due to their criminal activity, it triggers enhanced due diligence measures to mitigate risks. Such candidates or clients may be denied services by the company.


E-Communication and Voice Monitoring: To detect insider threats and unauthorized activities, companies employ e-communication and voice monitoring systems. These systems analyse unstructured data such as chat logs and voice recordings for signs of illicit behaviour. All communications conducted by employees within the company network or using company PCs will be monitored and recorded. In the event of detecting fraudulent transactions or illicit behavior, the company will access these recordings to identify the individuals involved in the criminal activity. For example, if an employee engages in suspicious conversations about manipulating market prices or leaking confidential information, it triggers alerts for immediate investigation.


Data Collection and Preprocessing: Fintech companies collect vast amounts of financial data from various sources, including transaction records, customer profiles, market data, and external APIs. Data scientists aggregate and integrate this data into a unified database for further analysis. Raw financial data often contains errors, missing values, and inconsistencies. Data scientists employ data cleaning techniques to eliminate noise and ensure data quality. They preprocess the data by transforming it into a suitable format for analysis. This data includes millions of transactions and financial processes and the database containing this information is updated every day with new transactions. It becomes difficult to interpret such large and complex data. In such cases data visualization can help to understand the structure or identify clues and patterns in the data. Popular data visualization tools are NetworkX, Gephi, NodeXL.


Creating a Network of Data: Through network analysis and visualization tools a comprehensive network of entities, transactions, and relationships within the financial ecosystem can be created. This network captures the interconnected nature of financial activities, enabling a holistic view of potential risks and threats.


Analyzing Network Dynamics: Once the network is established, various analyses can be performed to identify and assess potential risks. Analysing networks helps identify coordinated fraudulent activities that are hard to detect with traditional methods. Behavioural analytics can detect anomalous patterns in transactional data, such as sudden changes in transaction volumes or deviations from established norms.

Algorithms examine transaction records, user actions, and network activity to spot strange patterns that might signal a cyberattack. They're also trained to notice sudden changes in how employees access data. If anything seems off, it's investigated. This analysis helps create advanced security systems that add extra protection against unauthorised access, even if data gets leaked.

Network analysis techniques can uncover hidden relationships and connections between entities, revealing potential instances of collusion or illicit behaviour. It lets researchers see patterns and trends in how things are connected and how those connections affect the system as a whole.


Backtracking Suspicious Activity: By tracing the flow of transactions and connections within the network, AI algorithms can backtrack suspicious activity to its source. This process involves identifying key nodes and pathways in the network and analysing their interactions to pinpoint potential sources of illicit behaviour. Through this iterative process of analysis and investigation, fintech companies can take proactive measures to prevent financial crimes and protect financial integrity.

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