Financial crimes have been rising exponentially across the globe – not just in volumes, but also in terms of complexity and sophistication, with an estimated USD 2 trillion or more siphoned off through money laundering alone each year! Financial institutions (FIs) have been investing heavily in prevention, detection, investigation, and reporting of financial crimes on the one hand, while bearing the burden of direct losses as well as staggering remediation and compliance costs on the other as a result of failing to detect some such crimes!
FIs have been upgrading their legacy systems and optimizing manual processes to block financial crimes, as new technologies are emerging with unmatched complex data analysis and pattern detection capabilities – something which neither the legacy systems nor human intelligence is capable of! Artificial intelligence (AI) is now being increasingly deployed by banks to enhance the effectiveness of financial crimes compliance (FCC). In this article, we discuss the current pain points in the FCC landscape, explore the innovative AI-powered solutions that are emerging to combat such problems and the future of AI in strengthening FCC as it is envisioned to replicate and possibly surpass human intelligence.
Financial Crimes Compliance – The Current Challenges
Financial crimes compliance (FCC) is an extremely dynamic domain, with a plethora of regulatory bodies across jurisdictions, new regulations being enacted in quick successions and existing ones being made more stringent, increased focus on compliance governance, processes, reporting, and so on. While these steps are imperative in the face of growing complexity and volumes of financial crimes, FIs have been finding it an uphill task to keep pace with the changing regulatory requirements to remain compliant. The reason for this can be attributed in parts to some of the challenges banks currently face in terms of their FCC landscape – systems and processes – which have evolved over a period of time, and are not necessarily capable of accommodating the new and complex regulations in their current form.
- Lack of enterprise-wide single customer view – Fragmented and mismatched customer data residing in multiple systems makes it challenging for banks to have a 360-degree view of the overall profile and aggregate their transaction behavior across the bank.
- Lack of centralized golden data source across compliance functions and lines of businesses –The primary data requirements for FCC include customer, account, transaction, associated parties, alerts, and case investigation data. With no central data store in most FIs, data collation from multiple sources remains a cumbersome manual activity.
- High false-positive alerts generation – Legacy AML and fraud detection platforms use static rule-based triggers that generate a high volume of false positive alerts – in the range of 80-90%, while also being exposed to chances of slippage of true positives. Investigation and closure of false alerts consume a lot of manual effort and resources.
- KYC lifecycle management remains largely a manual function in most banks. From customer onboarding to regular screening, periodic reviews, and data remediation, manual processes result in delays and backlogs, human error and oversight, and high cost. Humans are also incapable of processing huge volumes of data with the speed expected of FIs in today’s dynamic world.
- Legacy systems lack the capability of combining structured and unstructured data, for generating a holistic view of customer behavior. Analytics of data from social media for example can provide early warning signals of suspicious activity. It is however near impossible to manually conduct such a level of research and analytics either.
- New Regulatory obligations – Adverse media screening, beneficial ownership identification, and corporate structure verification demand in-depth analysis of data that must be procured from multiple sources like global and local media, web, chambers of commerce, companies house, 3rd parties, and others. Manual processing of these is proving difficult and ineffective.
Reimagining Automated, Straight Through Compliance using AI
AI has the power to navigate through colossal amounts of data – structured and unstructured – and generate meaningful insights, which can then be used to improve the efficiency and effectiveness of systems and processes while reducing human intervention at the same time. Here are some emerging fintech trends across the 3 pillars of financial crimes compliance – prevention, detection, and investigation, that promise to reimagine the way this function is executed in FIs, while also attempting to resolve some of the current challenges in FCC.
1. The Prevention Pillar
1. Smart IDV and Beneficial Ownership graphs – Digital solutions to conduct automated ID&V are enabling automated straight through KYC for the retail customer through their own devices. Facial recognition, document scan, online forensic tests for document authentication, and forgery/ counterfeit are commonly used features in such automated solutions. AI solutions are also enabling dynamic visualization of complex corporate structures and their beneficial ownership, by integrating data from internal and external sources.
2. Entity resolution tools, capable of generating a single view of a customer maintaining multiple identities and relationships across the bank, are now being built on AI platforms, without having to overhaul any of the disparate legacy systems of the bank which hold the customer data. Sophisticated analytics are used for such matching, sometimes augmenting bank’s data with that of external 3rd party data to arrive at accurate matches.
2. The Detection Pillar
1. Automated adverse media screening – Transitioning from the traditional manual screening of customers for negative news and adverse media, AI-based contextual search and auto disposition solutions are enabling FIs to conduct a much wider search in terms of news and media coverage and to zero in on a small yet most likely number of potential true matches
2. AI-powered alert optimization and false positive reduction – Augmenting the rule-based detection platforms, machine learning-based dynamic pattern recognition can detect outliers even when they do not breach any defined alert triggering scenario. This is enhancing the effectiveness of alerts generated, leading to lesser false alerts and in some cases even suppressing alerts most likely to be false.
3. The Investigation Pillar
1. AI-based network and linkage analysis are becoming an increasingly important tool for banks to identify hidden relationships and criminal networks of their customers. Leveraging graph analytics, such solutions can also be used to generate corporate structures, revealing beneficial owners and shell companies. Such solutions are showing a lot of promise in the investigation of suspicious behavior alerted by the detection systems.
2. Customer self-service for auto resolution of fraud alerts is a strategy being adopted by FIs to reduce the manual investigation workload and expedite fraud alert resolutions. By opening up a customer self-service portal that integrates with the FIs’ investigation workflow, enabled through apps on customer devices, an alert disposition is getting automated. A feedback loop from this workflow back to the detection model is ensuring the non-recurrence of false alerts.
Towards High Tech, Low Touch Compliance – The AI Disruption
As criminals are getting more tech-savvy and finding new ways to commit financial crimes, and FIs embarking on implementing increasingly sophisticated crime prevention and detection mechanisms, the race to outsmart each other remains a continuous one! Leveraging advanced technology driven by data and powered by AI to generate insights is a trend that is gaining momentum in fighting fincrime. Processes are moving towards straight through, exception-based workflows, thus enabling Analysts to focus their time and attention towards an enhanced quality of investigation and decisioning rather than data collation. Digitization has made compliance functions like onboarding KYC and customer lifecycle management a contactless one, requiring human interaction only in case of an error or exception. The first line of FIs’ defense in the fight against financial crimes is slowly but surely undergoing digital disruption. The industry is busy working it all out – the journey from vision to reality is not too far!