4 Ways AI Can Future-proof Financial Services’ Risk and Compliance

The core function of a bank is to protect assets, identify risks and mitigate losses by protecting customers from fraud, money laundering and other financial crimes. In today’s interconnected and digital world, managing risk and regulatory compliance is an increasingly complex and costly endeavour. Regulatory change has increased 500% since the 2008 global financial crisis […]

The core function of a bank is to protect assets, identify risks and mitigate losses by protecting customers from fraud, money laundering and other financial crimes. In today’s interconnected and digital world, managing risk and regulatory compliance is an increasingly complex and costly endeavour. Regulatory change has increased 500% since the 2008 global financial crisis and boosted regulatory costs in the process. Financial Services Institutions (FSIs) are struggling to keep pace with new regulations like the updated Anti-Money Laundering Act, 2020, FRTB, 2023 and PSD2 in the EU. Complying with regulations, along with consumer-driven  calls for better data management and risk assessment, often translate to higher  operating costs for banks – as much as 60%.

Compliance problems are fundamentally data problems. What should sometimes be a simple reporting activity often turns into an operation nightmare due to the lack of ground truth to build these reports against and legacy technologies to run the same at scale. Given the fines associated with non-compliance and SLA breaches (banks hit an all-time high in fines of $10 billion in 2019 for AML), processing reports has to proceed even if data is incomplete. On the other hand, a track record of poor data quality is also “fined” because of “insufficient controls.” As a consequence, many FSIs are often left battling between poor data quality and strict SLAs, balancing between data reliability and data timeliness.

In addition to modernizing data management practice by using cloud based technologies, Artificial Intelligence (AI) is increasingly becoming relevant in regulatory compliance as it addresses common operations challenges and systematic issues that regulators face every day. There are countless potential benefits of technological breakthroughs in AI, but current regtech solutions have already demonstrated at least five clear benefits: regulatory change management, reducing false positives, fraud and AML prevention and addressing human error. This blog post will walk through each of these advantages and how AI can be game-changing for FSIs as they navigate the ever-evolving world of compliance.

1. Effective regulatory change management

To successfully deal with regulatory change management, financial services have to combine content from thousands of regulatory documents. Regulatory changes require adjustments that call for cooperation between different areas of the business and have second and third-order effects. For example, when asset managers restructure a fund or portfolio based on changes in regulations, each asset within it will be affected, resulting in necessary adjustments in other portfolios. When regulations are updated, there is a set of chain reactions.

The reporting for financial services also involves myriad documents and repetitive tasks. This is where natural language processing (NLP) and intelligent process automation (IPA) are valuable in meeting compliance requirements. Additionally, NLP can analyze and classify documents, extracting useful information such as client information, products and processes that can be impacted by regulatory change, thereby keeping the financial institution and the client up-to-date with regulatory changes. Automating the process of regulatory change management is a key use case of AI. The challenges facing financial firms, including hefty fines for non-compliance, can be addressed with successful AI implementation. In 2020 the SEC alone issued 715 enforcement actions, ordering those in violation to pay more than $4.68 billion combined.  The average fine was nearly $2M. AI’s ability to detect patterns in a vast amount of text enables it to form an understanding of the ever-changing regulatory environment, and pre-empt fines and associated costs.  

2. Reducing false positives

Financial institutions are experiencing large volumes of false positives that their conventional rule-based compliance alert systems are generating. Forbes reported that with false positive rates sometimes exceeding 90%, something is broken with legacy compliance processes. Large banks are experiencing false positives in their compliance systems at alarmingly high rates. Compliance alert systems based on standard regulatory technology are triggering thousands of false positives every day. Each of these false alarms must be reviewed by a compliance officer, which invites opportunities for inefficiency and human error.

The use of AI and machine learning to capture, extract and analyze several key data elements can help streamline compliance alert systems to near-perfection, thus addressing the problem of false positives. In this way, AI technology can improve the efficiency of compliance operations and reduce costs in today’s data-driven compliance environment, by autonomously categorizing compliance-related activities and alerting them to important updates, events and activities. As these technologies are built to learn from compliance officers’ own data, AI and ML applications can streamline compliance alert systems to near-perfection. AI technology can improve the efficiency of compliance operations and reduce costs in today’s data-driven compliance environment.

3. Enhance Fraud Prevention and AML with Anomaly Detection at Scale

Adoption of AI to combat fraud is already widespread — and will only increase with time. AI can monitor transaction history, combined with other structured and unstructured information, to identify anomalies that might indicate fraud, such as ATM hacks, money laundering, lending fraud, cyberattacks and financing of terrorism.

Identifying anomalies in data is a vital data understanding task. By exposing large datasets to ML tools and statistical methods, normal patterns in data can be learned. When inconsistent events occur, anomaly detection algorithms can isolate abnormal behavior and flag any events that do not correspond to the learned patterns. With millions of data points to analyze in compliance, FSIs need the computational power to ingest transaction, customer and process information in a scalable manner. Anomaly detection algorithms can help businesses identify and react to unusual data points in multiple scenarios. A bank security system may employ anomaly detection for the identification of fraudulent transactions or non-compliant practitioners.

Another application of AI/ML is in the generation of the alerts themselves. Traditionally these alerts have been generated based on a set of rules, most of which are hand-coded and a few rely on rudimentary data mining and statistical techniques. Some of these rules are obvious and are based on the value of a single input parameter or feature. For example, any transaction to sanctioned countries or above $10,000 must be reported and analyzed as part of existing AML policies. However, certain transactions should be scrutinized because of a subtle combination of the features (a typical AML scheme would be to wire funds just under the $10,000 mark). After all, there is a motivation to disguise and hide money laundering transactions. In addition, bad actors continuously come up with new and innovative ways to stay one step ahead of the monitors. If the monitoring system is based on how people have been able to beat the system in the past, it will fail to find new methods and techniques to cheat the system. Using graph analytics and AI, organizations can  find patterns invisible to the human eye or too subtle to be caught by existing rule sets, as well as correlate isolated anomalies into unique attack vectors by learning the context surrounding anomalous behaviours.

4. Human error mitigation

Human error costs regulated industries billions every year. For example, in 2020, Citigroup’s credit department employees made a clerical error which sent almost $1 billion to Revlon Inc.’s lenders. There are various causes of human error in asset management – ineffective processes, obsolete technologies or negligence to name a few. Financial regulations require compliance officers to track, manage and analyze detailed data about transactions, customers and operational activities at large banks. The volume of this information raises several opportunities for confusion that can easily give rise to human error. With regulatory compliance growing more technology-driven by the day, AI and ML applications can be invaluable in mitigating the impacts of human error.

AI and ML technologies can shed light on blind spots, reasonable errors, and other perspectives that humans may not necessarily pick up on. Further, good AI and ML programs can spot trends and patterns.

Today’s compliance problems are data problems. A modern approach to risk and compliance requires a robust data strategy defined by analyzing unprecedented volumes of data scalably, a transparent foundation for model risk management, and connecting  real-time insights for rapid response. With a modern data-driven strategy, FSIs can better respond to the most pressing risk and compliance use cases of compliance/risk monitoring,  regulatory reporting, fraud detection, KYC, and AML. Grounding compliance in data and levelling up with AI can future-proof compliance teams.

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Learn more in our upcoming event with our  Smarter Risk and Compliance with Data and AI workshop on October 13, 2021.

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