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Proactive fraud protection through advanced Analytics, AI and Machine Learning

Artificial intelligence (AI) has the potential to transform financial institutions (FIs), disrupting every aspect of financial services, from the customer experience to financial crime.

One of the most compelling use cases for AI is in the battle against financial crime. AI has two primary benefits for the banks engaged in this battle: it can increase the effectiveness and efficiency of financial crime investigations, and the institutionalise risk management.

Financial institutions can employ AI to analyse large amounts of data, to filter out false alerts and identify complex criminal conduct. It can identify connections and patterns that are too complex to be picked up by straightforward, rule-based monitoring, or the human eye.

This raises four fundamental questions:

  • Are financial institutions ready to embrace advances in ML to help uncover emerging patterns for preventing fraud?
  • How can financial institutions harness expanded data typologies generated by new authentication processes for better fraud detection?
  • How can financial institutions manage the data orchestration challenges to leverage different data sources, integrate with other information, and factor in decision making across the entirety of the customer journey?
  • Are financial institutions benefitting from better data orchestration?

Enhancing Anti-Money Laundering Monitoring and Driving Operational Efficiency

Digital transformation and artificial intelligence are undeniably changing the Anti-Money Laundering (AML) landscape. The need to modernise AML processes, coupled with a regulatory push towards innovation, is driving financial institutions to enhance AML monitoring & drive operational efficiency.

The manual and semi-automated nature of current AML compliance efforts slow down processing timelines and impact business productivity.

The immense volume of data that financial institutions are expected to comb through to meet regulatory requirements to detect and report suspicious activity becomes a daunting challenge.

The data is usually diverse and subpar. It’s common for systems to use only a subset of available data when generating alerts. Traditional transaction monitoring systems are unwieldy to maintain and rely on rules and thresholds that are easy for criminals to test and circumvent.

Investigation processes tend to be highly manual, from gathering the supporting data for a case to submitting a complete SAR (suspicious activity report). Meanwhile, the money launderers are working night and day to remain hidden, constantly engineering new ways to conceal the flow of funds.

Traditional Anti-Money Laundering (AML) and Combating Financing of Terrorism (CFT) tools and tactics take longer and cost more than they should.

To fortify the defences more efficiently and rapidly, financial institutions need ways to:

  • Automate tasks that formerly required human intervention, such as disposition of alerts
  • Detect more risks and effectively prioritise them with sophisticated analytics techniques
  • Provide richer context for investigations with access to more comprehensive insights

Financial institutions need to consider harnessing advanced analytics and AI technologies to enable a proactive, robust and unified strategy for enterprise-wide fraud and security intelligence.

Management must align fraud and cyber teams to increase cyber resiliency and minimise risk. They must deliberate how high-performance analytics and multiple detection methods can be used to monitor wider areas of risk in large volumes of data.

In the face of ever evolving and increasingly sophisticated cyber crime, integrating automation, AI and Machine Learning into financial crime programmes is essential. They facilitate more efficient transaction monitoring for suspicious activities and reduce false positives.

Developing new techniques for sound compliance practices for anti-bribery and corruption laws across jurisdictions is becoming more critical as technology moves fluidly across borders and international infrastructure.

Strategies to break down data silos, adjust to shifting regulations, and safeguard against present and future risks must form an integral part of critical event management strategy.

OpenGov Asia partners with key digital solution providers to explore how financial institutions can apply real-world AI and analytics applications to ensure a world-class integrated banking system that contributes to improve customer experiences, enterprise profitability, manage risk and regulatory compliance, anticipate fraud and create value from data.

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