The threat of financial fraud looms large as the world becomes more and more digitized. Fintech, lending, and insurance companies are having a hard time navigating the challenges posed by the new digital landscape. Put another way, the need to protect their customers from the far-reaching consequences of financial fraud has become paramount.
Are Financial Institutions Equipped to Handle the Rising Tide of Financial Crime?
“Based on the date of occurrence of frauds, advances-related frauds formed the biggest category before 2019-20. Subsequently, in terms of the number of frauds, the modus operandi shifted to the card or internet-based transactions,” noted a recent RBI’s Report on Trend and Progress of Banking in India. According to Cybersecurity Ventures, cybercrime is projected to cost the world 8 trillion USD in 2023, making the need for robust transaction monitoring systems more crucial than ever.
The question is, are financial institutions equipped to handle the rising tide of financial crime? Are they able to monitor transactions in real-time and identify red flags before it's too late? Can they keep up with the constantly evolving tactics of fraudsters?
What is Transaction Monitoring Today: The Role of ML and AI
Transaction monitoring is the practice of keeping tabs on financial transactions to detect and prevent fraud, money laundering, and financial crimes. While the definition hasn’t changed, how companies manage and conduct transaction monitoring has changed. Initially, it was about banks and financial institutions keeping a watchful eye on their transactions, looking out for any funny business, like check and credit card fraud. Except, most of it was done manually. As technology advanced, monitoring became more sophisticated. In the 80s and 90s, computers were brought in to make fraud detection effective. Today, thanks to ML and AI, financial institutions can sift through swathes of data to uncover suspicious patterns.
Monitoring transactions is imperative for BFSI, FinTechs, Lending, Insurance Companies and neo-banking companies. In 2019, Deutsche Bank was fined $150 million by the New York Department of Financial Services for failing to detect and report suspicious activities. In 2020, Standard Chartered Bank had to pay $1.1 billion for its Anti-Money Laundering compliance failures, and US Bank was slapped with a fine of $185 million by FinCEN over Anti-Money Laundering compliance failures. Meaning, if you don't keep an eye on your transactions, the regulators will do it for you - and it won't be cheap.
State of Play: Transaction Monitoring for Fintech, Lending, Insurance and Neo-banking firms
At the heart of it, transaction monitoring is all about understanding the risk levels of customer activity. By tracking risk levels, banks can generate Suspicious Activity Reports (SARs) based on current activity and future predictions. Transaction monitoring helps banks safeguard their customers' financial health and stability while contributing to the overall stability of the financial system.
Despite the increasing rules and regulations to monitor high-risk transactions, scammers are proving to be elusive. For fintech Lending, Insurance and Neo-banking companies, this poses a huge challenge as identifying the underlying issues and recalibrating the rules to prevent fraud is a painstaking and complex process. But AI is revolutionizing the way fintech companies monitor high-risk transactions and fend off financial fraud.
From customer onboarding to ongoing transactions and behaviour monitoring, lending firms face a lot of challenges. Transaction monitoring can help lending firms streamline their processes, automate customer onboarding, and detect red flags on time. These solutions help the firms operate with confidence and peace of mind.
The sheer number of transactions makes insurance companies an attractive target for money launderers. The fallout from such incidents includes monetary losses, fall in stock prices, reputation damage, and the loss of business, etc. By implementing effective Anti-Money Laundering measures and staying alert to the signs of financial fraud, these organizations can safeguard their reputation, secure their financial stability, and maintain the trust of their customers and stakeholders.
Types of Transaction Monitoring
Fraud-based Monitoring uses ML algorithms to parse copious amounts of transaction data and detect patterns suggesting fraudulent activity. This allows financial institutions, merchants, and organisations to protect their assets and customers from money laundering, credit card fraud, identity theft, etc.
Anti-Money Laundering (AML) based monitoring uses ML algorithms and analytical tools to crunch large amounts of data to identify any patterns or anomalies suggesting money laundering activity. Anti-Money Laundering-based monitoring helps to comply with laws and regulations on anti-money laundering and countering the financing of terrorism (CFT).
However, Anti-Money Laundering-based transaction monitoring comes with its own set of challenges. First up, false positives. A recent study found that 57% of the cost of compliance is labour, owing to the additional work required to sort through these false alerts. Additionally, using a single, broad risk scenario for a wide variety of customers and behaviours is unwieldy. Such oversimplification can lead to an increase in false positives and flawed tracking. And then there's the issue of too many scenarios. Finding the right balance and being mindful of the potential challenges is important when you deploy your Anti-Money Laundering transaction monitoring strategy.
Bureau for Detecting Fraud and Money Laundering
Bureau, a transaction monitoring platform, offers a simple, user-friendly solution to detect patterns across specific data points over time. Our drag-and-drop functionality allows users to easily flag any deviations from expected behaviour, making it an efficient tool for identifying potential fraud or money laundering.
The platform monitors customer transactions, along with their Know Your Customer (KYC) details, to identify potential money mules and criminals attempting to launder money or use it for illegal activities such as terrorism. Analysts use the platform's Case Management system to review suspicious transactions and help organizations stay on top of potential risks.
Plus, Bureau's rule engine automatically flags risky transactions based on predefined rules, linking them to identities and creating reports in case of fraudulent activity. This allows organizations to quickly identify and address potential threats. Bureau also offers analytical and reporting solutions that are shared with internal and external parties for auditing purposes, providing organizations with a 360-degree view of their transaction monitoring efforts.
Transaction monitoring also plays a vital role in helping organizations identify and manage potential risks associated with transactions, including transactions with high-risk individuals or countries.
Transaction Monitoring Techniques
Rule-based monitoring uses pre-defined rules or algorithms to identify suspicious activity and is configured to flag shady transactions.
Behavioural monitoring uses machine learning algorithms to analyze transaction data and identify abnormal or suspicious behaviour. It detects patterns or anomalies that are not easily identifiable using rule-based monitoring.
Link analysis uses network analysis to identify relationships between different entities involved in the transaction and spot patterns or anomalies rule-based monitoring might miss.
Anomaly detection uses statistical models to identify transactions that stray from normal behaviours.
Network-based surveillance allows organizations to monitor high-risk indicators and prioritize their attention on the most pressing cases.
Bureau uses advanced analytical tools to sort through colossal amounts of data to identify patterns and connections that might otherwise go unflagged. By focusing on the most important cases, organizations cover more ground and catch more bad actors.
The system streamlines the workload of compliance teams by reducing the number of false positives leading to less operational stress and saving on manpower costs. This lets organizations tackle the challenges posed by errors of omission and commission, resulting in a substantial improvement in accuracy and detection capabilities.
The risk-based approach of network-based surveillance has two key components: risk assessment and due diligence. By customizing the due diligence process to each customer's unique risk profile, companies flag suspicious activity, adjust monitoring rules based on the learned insights, and identify false positives for accurate reporting.
Conclusion and Future Outlook
The need for effective transaction monitoring has never been greater. As the regulatory landscape continues to evolve, fintech, lending, and insurance companies must stay ahead of financial fraud to maintain the trust of their customers and stakeholders. Bureau's transaction monitoring bundle help companies fight financial crimes by reducing false positives, increasing efficiency and efficacy, and building credibility with regulators.
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