Fighting A Losing Battle Against Financial Crime? Use Data Science

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Ever-changing compliance regulations and state intervention keep financial services sectors on their toes in terms of prioritising governance of financial crime. Regulators require businesses and banks to ensure that their approach to tackling financial crimes works. Evidence of such may include relevant statistics and suspicious activity reports (SARs) based on factors such as size, organisation type and geographical location.

Financial misconduct is evolving, now encompassing sophisticated money laundering, corruption, market abuse and financing terrorism. A recent development is “smurfing” – preventing the generation of a currency transaction report by minimising the transaction to USD10,000.

Tackling financial crimes costs nearly USD1.3tn annually, according to a 2018 survey by Refinitiv. Regulators imposed USD26bn worth of fines that year for non-adherence to KYC, AML and other sanctions. Dealing with financial crime is, therefore, beneficial from both an organisation’s efficiency standpoint and a regulatory viewpoint.

Manual intervention

Meeting compliance requirements of global regulators can be a daunting task, but manual intervention exacerbates the challenges. For instance, it has become difficult to physically manage the significant amount of data institutions generate in terms of, for example, financial transactions, credit approvals and user engagement.

Static rules and human intervention play a crucial role in traditional AML practices, but the large amounts of data make it unsustainable. Furthermore, human error and deliberate fraudulent activities pose risks to the entire organisation.

Modern challenges

As financial crimes continued to evolve, organisations needed to deploy safeguards and employed teams of investigators and analysts to use tools to do so. It requires handling multiple compliance regulations and large amounts of data, and AML analytics and other machine-learning tools became viable options for banks to tackle such challenges. These efforts to streamline processes have also helped to reduce risk-alert backlogs, enhanced regulatory compliance and the customer experience, and lowered operational costs.

Employing innovation – AML analytics

AML analytics activity employed by banks and financial institutions involves a significant number of false positives. It is also not ideal to have analysts spend their time managing cases and prioritising risk alerts. Automating certain parts of the tasks by employing artificial intelligence, machine learning and blockchain technological tools can help save time to assess riskier cases.

What analytics tools offer

Predictive analytics

Predictive analytics has emerged as one of the most effective ways of identifying financial crime. Predictive analytics helps make real-time predictions for the purpose of forecast reporting and is useful in assessing financial crime-related trends. Banks and businesses with large numbers of transactions and clients could use it to make more informed decisions.

Data visualisation

Data visualisation enables AML analytics professionals and management to look at complex financial datasets via a sophisticated interface. It helps streamline the identification of inaccuracies in data and trends that suggest fraudulent activity.

Crime network analysis

Financial crimes are often not isolated incidents and involve extensive networks. A social network analysis enables a bank to look at trends and identify those involved. Analysing the system independent of the network is also beneficial. Analytics tools can provide entity resolution of individuals with multiple forms of relationships and identities with a bank.

Solutions

Banks could meet regulatory timelines by streamlining their AML activity. Solutions also include minimising false positives by nearly 95%. Ratifying legacy models and providing modifications according to requirements lead to better customer segmentation.

A breakdown of solutions:

  1. Transaction monitoring and supervision
  2. Post-transaction assessment of alerts
  3. Reviewing and minimising false positives
  4. Evaluating flagged transactions
  5. Flagging unusual or suspicious trades
  6. Filing support for suspicious activity reports (SARs)
  7. Documenting findings in SARs and incorporating trackers and maintenance

Conclusion

 

Regulatory compliance requires that financial institutions and services firms provide evidence of the effectiveness of AML tracking programmes. AML analytics tools are becoming increasingly important for firms to keep track of fraudulent activity and monitor transactions. Banks and other businesses can employ such tools to address the challenges posed by increasing financial crime.


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