How Emerging Technologies Are Disrupting the Banking Compliance Landscape

Financial crimes across the globe have steadily risen in the last two decades – not just in volume, but also in terms of complexity and sophistication. In pursuit of enhanced compliance, banks have started moving from rule-based to risk-based compliance, with massive volumes of data processing required for risk-based decisioning. As new technologies in the form of analytics, machine learning, artificial intelligence, robotics and several others started emerging and maturing, banks and financial institutions (FIs) began assessing the these technologies could bring to regulatory compliance. This led to the emergence of regtech (regulatory technology); by leveraging these new technologies tailor-made for compliance, FIs can enhance both efficiency and effectiveness.

The new age digital technologies come with unmatched complex data analysis and pattern detection capabilities – something neither legacy systems nor human intelligence is capable of! We have, in recent times, been witnessing heavy investment in prevention, detection, investigation and reporting of financial crimes by leveraging some of these cutting-edge technologies. Here, I explore how emerging technologies are revolutionizing banking compliance, as well as the future roadmap of regtech in transforming this function.

The Current Challenges in the Banking Regulatory Compliance Landscape

The banking and financial services industry is among the most heavily regulated industries, with a plethora of local and global regulatory bodies across numerous jurisdictions. FIs are continuously grappling with new complex regulations that require them to strengthen their compliance frameworks, governance, oversight, procedures and platforms. Most banks lack a formal regulatory change management framework – a governance imperative in the current dynamic regulatory environment. As cost of compliance continues to remain very high – estimated to be over $80 billion annually – reducing compliance costs while improving operational efficiency in this function is one of the major challenges banks have been focusing on.

Some of the key challenges in banking compliance include:

• Fragmented data across compliance functions and lines of businesses. Without a “golden source,” it is difficult for banks to process and analyze the required data for compliance and oversight.
• Lack of an enterprise-wide single customer view. A single customer’s multiple relationships residing in multiple systems of the bank causes a challenge in holistic monitoring and analysis of customer behavior.
• High volume of false positive alerts in AML and fraud control. In the range of 90 percent or more for most large banks globally, this is one of the key pain points caused due to usage of legacy rule-based detection systems. This also exposes banks to slippages on true alerts, which is a compliance violation.
• Manual, effort-intensive processes, like KYC activities and alert investigations. These activities are time-consuming, error-prone and costly, and they constitute a bulk of the compliance function in banks.
• Legacy systems are incapable of combining structured and unstructured data. Unstructured data holds a huge amount of hidden information about customer behavior. Emails, chats, web and social media clips of customers, if analyzed in the right context, can generate early warning signals of suspicious behavior.

The Digital Technologies Disrupting Banking Compliance

Digitization has become a buzzword across industries – be it in manufacturing, retail, automobiles, telecom, banking or any other! New technologies are enabling businesses to work more efficiently, reducing time and cost while delivering enhanced value to customers. It’s no wonder, then, that these technologies are also being explored for internal functions like compliance in banks and FIs to address current challenges as well as for future-proofing KYC (know your customer), AML (anti-money laundering) and fraud control.

FIs are leveraging emerging technologies in compliance for enhanced effectiveness, efficiency, accuracy and cost optimization, while delivering abundant additional value in the process. Some of those technologies include:

• Analytics-based entity resolution These are capable of generating a single view of a customer maintaining multiple identities and relationships across the bank, without having to overhaul any of the bank’s disparate legacy systems holding the customer data. Sophisticated analytics are used for such matching, sometimes augmenting the bank’s data with that of external third party data to arrive at accurate matches.
• AI-based network and linkage analysis. This is becoming an increasingly important tool for banks to identify hidden relationships and networks through which financial crimes might be committed. Banks are using such tools to generate insights on suspicious hidden relationships and criminal networks. These tools link the data available in the bank – including unstructured customer data in the form of mails and chats – with the data available in external sources, including social media, to unearth suspicious networks. Such networks can then be red flagged by the bank and subjected to greater scrutiny for detecting financial crimes.
• Machine Learning (ML) based platforms in optimizing alerts and reducing false positives. These platforms have already delivered great results in financial crimes detection and investigation. ML-based alert generation solutions can discover suspicious behavior leading to money laundering and fraud and can detect outliers even when they do not breach any defined alert triggering scenario – something rule-based platforms are incapable of. Some banks are also using ML tools to automatically discount false alerts generated by the rule-based systems using dynamic rules created by the ML algorithm.
• Automated ID and document verification Using mobile image processing with deep-convolution biometric face matching, automated ID and document verification tools are enabling straight-through processing of KYC. After installing these applications in their phones, customers can operate them directly to upload ID documents and selfie videos as part of KYC during onboarding, as well as during periodic and ad-hoc reviews.
• Biometrics and computer vision technology for fraud prevention. These solutions are gathering momentum, even as online and card fraud continues to increase. Multi-factor authentication, using the customer’s fingerprint, face or iris scan, voice or keyboard dynamics is being enabled in various payment channels, as such inherent customer biometrics can be very difficult to steal or replicate and can help prevent payment fraud. Machines’ speech and visual recognition capability is steadily improving, and it is expected to exceed that of humans in the next few years!
• Robotic process automation (RPA) in KYC and AML functions like onboarding, alerts and case management are fast becoming the norm in FIs. The software robots are programmed to mimic human steps, which are repetitive and rule-based, and can complete tasks involving accessing multiple systems, data entry and collation, report generation and so on – just like humans, but at a fraction of the time and cost. Standard Bank, Africa’s largest bank, has used RPA combined with AI-based cognitive automation to bring down the client on-boarding and KYC timelines from 20 days to five minutes!

Emerging Technology in Compliance: The Way Forward

As criminals are finding increasingly sophisticated means to commit financial crimes, and as banks are embarking on more stringent crime prevention and detection rules, the race to outsmart each other has been continuous. The day may not be too far off when a single digital platform will carry out end-to-end compliance functions, as machine learning and analytics are integrated with RPA and chatbots, NLP (natural language programming)-based text mining and deep-learning-based image recognition systems complete the automation landscape.

However, a revolutionary innovation would be to develop machine intelligence that can pre-empt what criminals might do to bypass banks’ prevention and detection mechanisms to commit financial crimes and to create algorithm-based rules accordingly for blocking such crimes.

Can we expect AI to create its own threat intelligence list and generate alerts when transactions involving such individuals/entities are initiated? Can NLP be built to interpret and analyze new regulations and to create rules for changes to be implemented in the bank’s systems and processes as a result?

Banks globally have just started to tap the potential of advanced technology for regulatory compliance, and regtech innovation is bound to take the industry by storm, completely transforming the way compliance is managed in banks and FIs. This may not be a distant dream, as the journey has already begun!