Transaction Monitoring in Fintech: Challenges and Solutions

4 mins

While the COVID-19 pandemic has affected the global economy with long-lasting implications for companies and consumers, the Fintech sector has largely been resilient with notable growth across most geographies. A joint study of the sector by the World Bank, the Cambridge Centre for Alternative Finance at the University of Cambridge’s Judge Business School, and World Economic Forum identified Fintech firms have continued to help expand access to financial services during the COVID-19 pandemic—particularly in emerging markets. “Fintech has shown its potential to close gaps in the delivery of financial services to households and firms in emerging markets and developing economies,” said Caroline Freund, World Bank Global Director for Finance, Competitiveness and Innovation. She added that the fintech industry is “adapting to the pandemic and offers insights for regulators and policymakers seeking to promote innovation and reap the benefits of fintech while managing risks to consumers, investors, financial stability, and integrity”.

Operational Challenges

Meanwhile, fintech companies are also facing operational challenges due to the pandemic, according to the study. Forty per cent of firms surveyed noted that they have either introduced or are in the process of introducing better fraud or security measures as a response to business conditions under the pandemic. In fact, regulators have also started worrying about compliance within fintech firms given their increased popularity and growth. They are concerned that neobanks’ compliance processes are not up to the mark, leaving them vulnerable to abuse by criminals.

Recently, German regulator BaFin ordered neobank N26 Bank to “rectify deficiencies both in IT monitoring and in customer due diligence”. The order stated that the neobank should hire more personnel, improve the documentation of compliance processes, redo due diligence checks on some existing customers and close backlogs in transaction monitoring. BaFin also appointed a “special commissioner” to update the regulator on the bank’s progress. N26, which has 7 million customers in 25 markets, clarified that fraudsters were misusing its platform by pushing third parties to open accounts, which were then used for criminal purposes. “Since the start of the COVID pandemic, criminal activity in connection with online trade has increased dramatically around the world,” the neobank said. “The demands of BaFin aim to prevent the opening of such accounts, to identify illegal financial transactions as quickly as possible, and to block them.”

The need for Transaction Monitoring

Transaction monitoring refers to the process of monitoring customer transactions such as deposits, withdrawals and transfers for potential illegal behaviour while taking into account customers’ historical and current information and transactional relationships. Today, financial institutions make use of various software solutions to analyse transactions as it is not practical for them to have dedicated staff to review each and everyone from millions of transactions. These transaction monitoring systems have various rules and threshold settings to identify potential illegal behaviours and flag those transactions. When a transaction is flagged, a human investigator goes into its details, decides whether it is suspicious or not and reports to relevant authorities if it is suspicious. Transaction monitoring is important for any regulated financial institution as the process acts as a key line of defence against all forms of financial crimes.

Issues with financial crime detection

Criminals across the world continue to innovate on their strategies to launder money despite increased scrutiny by regulators and higher compliance spend by financial institutions. They know how transaction monitoring systems work within financial institutions and have ways to easily bypass rules and thresholds. To counter new and emerging money laundering fraud strategies, financial institutions need to create new rules and update thresholds. Financial institutions need several weeks to create, test and implement new rules into their transaction monitoring systems and rules upgrade may not be a sustainable option given the frequent changes in the regulatory landscape continuous changes in criminal behaviour.

Operational efficiency is impacted

On the operational side, financial institutions have the problem of false alerts. While lenient transaction thresholds in monitoring systems would help financial institutions avert risk, they result in a large number of false alerts. While each and every alert needs to be investigated, the pile of false alerts leads to wastage work hours and huge alert backlogs for many institutions. The inability to determine the relative risk of individual alerts adds tremendous operational pressure to AML teams.

AI for superior AML compliance programs

AI has brought in disruption in many industries with its ability to mine, structure and analyse huge volumes of data and provide actionable insights. AI can take up repetitive tasks, saving valuable time, effort and resources that can be redirected perform higher-value functions. From an AML compliance perspective, AI can extract risk-relevant information from large volumes of data and present that information in a better coherent manner, making the process of identifying high-risk transactions even easier in the fight against financial crime.

Machine learning enables superior data analytics which can accurately flag anomalous behaviour. The process inefficiencies in AML alert management can be reduced significantly as AI can effectively automate repetitive tasks, saving a lot of man-hours for financial institutions and eliminating alert backlogs. With advanced machine learning, alerts can be grouped based on risk levels so that more time and talent can be dedicated to those that matter. While AI cannot replace human judgment in the AML compliance process, it can assist humans with predictions, recommendations and powerful analytics, enabling faster and accurate decision making.

Comprehensive risk coverage with Tookitaki

Tookitaki has developed its Anti-Money Laundering Suite (AMLS), a next-generation solution that is powered by advanced Machine Learning and Big Data analytics. AMLS is purpose-built to cut through the noise and clutter generated by legacy AML transaction monitoring and screening processes. It provides an automated alert triaging function, called Smart Alert Management and a financial crime detection function, called Intelligent Alert Detection. AMLS is validated by leading global advisory firms and banks across Asia Pacific, Europe and North America.

Our AML transaction monitoring solution is powered by an AML typology repository that federates industry learning in a privacy-protected manner. We have the largest global typology library that has been built across regulators and financial institutions globally. Our typology library is regularly updated to help you stay on top of new patterns in money-laundering behaviour and changes to regulations globally. As your business grows and your appetite to risk changes, you can easily configure and test new rules and scenarios in a safe environment before you move into production.

The solution also features an efficient alerts management process through auto threshold generation, simulation engine and smart alerts management. It reduces compliance teams’ effort on ongoing model maintenance by 70-80% through automatic generation of risk indicators and threshold values. It has a built-in simulation mode functionality to significantly reduce the time it takes to test and deploy new rules.

Our solution has been proven to be highly accurate in identifying high-risk transactions. For more details of our transaction monitoring solution and its ability to identify various money laundering techniques, please contact us.

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