The UK banking sector is rethinking how it detects financial crime, moving beyond a traditional reliance on large volumes of alerts and rule-based systems towards more intelligent, risk-based models. Vedanarayan Bhat, a banking professional with expertise in customer risk-rating and financial crime data models, has worked on banking programmes in the UK and globally. His experience includes helping financial institutions rethink how customer risk is assessed and how financial crime teams can focus their attention on more meaningful alerts.&nbsp;For years, financial crime detection in banking has largely been a story of volume: greater numbers of alerts, more rules and more analysts hired to deal with backlogs that continue to grow faster than the number of people available to investigate them. That approach is now changing, with banks increasingly exploring risk models that are evaluated not only by the number of alerts they generate, but also by the accuracy and usefulness of those alerts.&nbsp;The shift is significant. Legacy customer risk-rating systems developed over the past decade to support Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements were intentionally designed to be cautious, based on the assumption that a false alarm was preferable to a missed one. The result has been investigators dealing with large numbers of poor-quality alerts, potentially making it harder to identify the broader picture of suspicious activity.As Bhat explains, the industry developed systems that were very useful for alert generation, but not necessarily as effective for alert identification.&nbsp;Instead of relying on increasingly sophisticated rule sets alone, many financial institutions are now turning towards risk models that consider multiple factors, including geography, transaction behaviour and customer type, to produce more accurate risk assessments. The objective is not to reduce scrutiny, but to provide investigators with better-informed alerts.&nbsp;A UK banking programme involving Bhat's work on a revamped customer risk-rating system illustrates this approach. The project focused on more intelligent weighting of different aspects of risk according to customer type, geography and other factors. According to the article, this enabled the organisation to reduce false positives by 35%, while giving Financial Crime teams approximately 40% more capacity to work on suspicious transactions. “The objective is not to decrease alerts. It's better alerts that investigators can investigate and act on in a timely manner,” Bhat says.&nbsp;The commercial importance of this approach is also becoming increasingly evident. Financial crime and compliance (FC&amp;C) teams are among the more resource-intensive functions within a bank, while manual reviews can add time and friction to the customer experience. A transaction that is ultimately found to be valid can nevertheless result in payment delays, account freezes or additional customer inquiries when it has been flagged for investigation.&nbsp;At the same time, regulatory expectations continue to evolve. The Financial Conduct Authority has indicated the importance of risk models that are effective, transparent and auditable. Organisations are increasingly expected to demonstrate how customer-risk decisions are reached rather than relying solely on conservative rule-based approaches.&nbsp;“Explainability isn't a nice-to-have anymore,” Bhat says. “But if you don't present a regulator with the reasoning behind a risk score, then it's not because you are being cautious that you are safe. It's because you don't have something defensible.”&nbsp;The emerging approach represents a shift from the long-standing focus on generating more alerts to using better data, stronger risk modelling and more transparent decision-making. For banks dealing with increasingly digital and transaction-heavy environments, more intelligent, risk-based financial crime detection can help investigators focus on relevant cases while supporting customer protection and compliance objectives.

For years, financial crime detection in banking has largely been a story of volume: greater numbers of alerts, more rules and more analysts hired to deal with backlogs that continue to grow faster than the number of people available to investigate them.

The UK banking sector is rethinking how it detects financial crime, moving beyond a traditional reliance on large volumes of alerts and rule-based systems towards more intelligent, risk-based models. Vedanarayan Bhat, a banking professional with expertise in customer risk-rating and financial crime data models, has worked on banking programmes in the UK and globally. His experience includes helping financial institutions rethink how customer risk is assessed and how financial crime teams can focus their attention on more meaningful alerts. For years, financial crime detection in banking has largely been a story of volume: greater numbers of alerts, more rules and more analysts hired to deal with backlogs that continue to grow faster than the number of people available to investigate them. That approach is now changing, with banks increasingly exploring risk models that are evaluated not only by the number of alerts they generate, but also by the accuracy and usefulness of those alerts. The shift is significant. Legacy customer risk-rating systems developed over the past decade to support Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements were intentionally designed to be cautious, based on the assumption that a false alarm was preferable to a missed one. The result has been investigators dealing with large numbers of poor-quality alerts, potentially making it harder to identify the broader picture of suspicious activity. As Bhat explains, the industry developed systems that were very useful for alert generation, but not necessarily as effective for alert identification. Instead of relying on increasingly sophisticated rule sets alone, many financial institutions are now turning towards risk models that consider multiple factors, including geography, transaction behaviour and customer type, to produce more accurate risk assessments. The objective is not to reduce scrutiny, but to provide investigators with better-informed alerts. A UK banking programme involving Bhat's work on a revamped customer risk-rating system illustrates this approach. The project focused on more intelligent weighting of different aspects of risk according to customer type, geography and other factors. According to the article, this enabled the organisation to reduce false positives by 35%, while giving Financial Crime teams approximately 40% more capacity to work on suspicious transactions. “The objective is not to decrease alerts. It's better alerts that investigators can investigate and act on in a timely manner,” Bhat says. The commercial importance of this approach is also becoming increasingly evident. Financial crime and compliance (FC&C) teams are among the more resource-intensive functions within a bank, while manual reviews can add time and friction to the customer experience. A transaction that is ultimately found to be valid can nevertheless result in payment delays, account freezes or additional customer inquiries when it has been flagged for investigation. At the same time, regulatory expectations continue to evolve. The Financial Conduct Authority has indicated the importance of risk models that are effective, transparent and auditable. Organisations are increasingly expected to demonstrate how customer-risk decisions are reached rather than relying solely on conservative rule-based approaches. “Explainability isn't a nice-to-have anymore,” Bhat says. “But if you don't present a regulator with the reasoning behind a risk score, then it's not because you are being cautious that you are safe. It's because you don't have something defensible.” The emerging approach represents a shift from the long-standing focus on generating more alerts to using better data, stronger risk modelling and more transparent decision-making. For banks dealing with increasingly digital and transaction-heavy environments, more intelligent, risk-based financial crime detection can help investigators focus on relevant cases while supporting customer protection and compliance objectives.

For years, financial crime detection in banking has largely been a story of volume: greater numbers of alerts, more rules and more analysts hired to deal with backlogs that continue to grow faster than the number of people available to investigate them.

UK banks are rethinking financial crime detection because of algorithm

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UK banks are rethinking financial crime detection because of algorithm