Tapping into the Power of AI & ML to Combat Financial Crime

1. Tapping into the Power of AI & ML to Combat Financial Crime Neelesh Pal Corporate Vice President, Banking and Financial Services Financial Crime Shanmugaraj Rajamani Director – Solutions,

2. In today's ever more interconnected and digitized landscape, the challenge of financial crime has grown in complexity and scale due to technological advancements and the growing sophistication of criminals. Traditional manual compliance methods are proving inadequate in effectively countering these evolving threats. According to Celent, a leading research firm, financial institutions are projected to spend a staggering USD 58.0 Billion in 2023 on Anti-Money Laundering (AML) efforts.1 Furthermore, regulatory pressure is mounting, as evidenced by historic fines totaling USD 38.47 Billion since 2000. Of this, USD 21.47 Billion is attributed to AML violations, while USD 16.9 Billion is related to sanctions violations.2 Highest AML Fines - 21st Century (in US$ billions) Highest Sanctions Fines - 21st Century (in US$ billions) 8.9 7.2 2.6 1.62 1.6 1.45 0.913 0.9 0.787 0.611 Westpac HSBC Goldman Sachs* JP Morgan ING Unicredit Stan Chart* BNP Paribas Commerzbank Credit Agricole *GS Fine made up of 5 fines from 5 separate countries in connection with 1MDB from USA, HK & Malaysia. Note: Most frequently fined (aggregating in country agency fines) involve 1) Deutsche Bank (7 fines), Goldman Sachs (5 fines), Credit Suisse (4 fines) & JP Morgan (3 Fines) *Stan Chart amount is primarily made up of fines in 2012 and 2019 (with common antecedents). Note: Most frequently fined Banks (by country): UK: 4 Banks; France 3 Banks; Germany 2 Banks; Netherlands 2 Banks; Switzerland 2 Banks: Note: UK does not include Nat West fine in 2012 as related to prior ABN AMRO activity. Source: Financial Crime News Figure 1: Fines Resulting from Violations To confront this dynamic threat landscape, integrating Artificial Intelligence (AI) / Machine Learning (ML) into financial crime compliance is critical. These cutting-edge analytical capabilities enable financial institutions to fortify their defenses against money laundering, fraud, insider trading, embezzlement, cybercrime, Ponzi schemes and other illicit activities while improving operational efficiency, accuracy and adaptability. This article delves into the transformative potential of AI and ML in managing financial crime compliance, heralding a future of smarter, more resilient financial crime prevention and regulatory adherence. 1CELENT 2Financial Crime News wns.com 01

3. Challenges in Financial Crime Compliance The financial crime industry faces significant challenges due to escalating prevention costs and stringent global regulatory requirements, such as the Bank Secrecy Act (BSA), Foreign Account Tax Compliance Act (FATCA), Dodd-Frank Wall Street Reform and Consumer Protection Act, among others. Moreover, frequent updates and changes to regulations, such as those related to Ukraine-Russia war sanctions and General Data Protection Regulation (GDPR), further compound the complexity of the landscape. According to the 2023 Cost of Compliance report by Thomson Reuters Regulatory Intelligence (TRRI), nearly 73 percent of respondents expect an increase in regulatory activity – with 27 percent anticipating it to be significantly more.3 The report also reveals that more than 30 percent of respondents anticipate the size of their compliance teams to increase over the next 12 months. Notably, ~60 percent expect the cost of senior compliance officers to surge. Some of the common challenges financial institutions face include: Key Challenges Multiple Siloed Systems Banks have legacy systems and multiple downstream systems, leading to high lead time Data Quality Issues Poor-quality data leads to multiple challenges – high false positive, failure to flag true risk, excessive manual effort and more Longer Onboarding / Investigation Timelines High manual effort and process inefficiencies lead to delay in onboarding / investigation Lack of Skilled Resources Meeting the escalating demands in the FinCrime space is a challenge due to the scarcity of skilled resources High False Positive Investigating excessive false positives results in significant labor costs Frequent Regulatory Changes Need to adapt to evolving regulatory changes Figure 2: Key Financial Crime Compliance Challenges 3Thomson Reuters wns.com 02

4. The Transformative Potential of AI & ML AI / ML solutions can emulate human-like decision-making and perform complex tasks. According to the Institute of International Finance (IIF), AI and ML are critical tools to combat rising fraud in payments, especially in the wake of losses totaling USD 28.58 Billion in 2020 and projected to grow to USD 49.32 Billion by 2030.4 institutions can address various challenges related to the efficiency and effectiveness of preventing financial crime. One of the latest advancements gaining significant attention is Generative AI, which holds tremendous promise in the financial crime industry. Numerous financial institutions and technology firms have taken proactive initiatives to develop solutions and become pioneers in adopting this cutting-edge technology. AI and ML are pivotal in resolving intricate scenarios, making them indispensable for detecting unfamiliar transaction patterns to combat fraud and money laundering. These technologies also prove invaluable in identifying alternative channels to gather information and documentation for conducting effective Know Your Customer (KYC) reviews. Moreover, they enable early warning signals, enhancing the ability to pre-empt money laundering and fraud instances. Similarly, major technology firms like Google are making significant strides in the highly domain-intensive regulatory market by introducing specialized products to address challenges faced by financial institutions, harnessing the power of AI. For instance, Google Cloud recently launched the Google AI Anti Money Laundering Tool, an AI-powered transaction monitoring platform that replaces the traditional manually-defined, rules-based approach.5 AI / ML technology encompasses various models / tool stacks, each serving unique purposes in combating financial crime. These include Anomaly Detection, Pattern Recognition, Behavior Analysis, Natural Language Processing (NLP), Risk Scoring and Prioritization, Data Integration and Management, Cognitive Computing, Link Analysis, Customer Segmentation, Predictive Analytics, Generative AI and many other cutting-edge innovations. By combining these capabilities with best-in-class workflows, tools and databases, financial This innovative tool leverages financial institutions' vast data to train advanced ML models, providing a comprehensive view of risk scores and effectively flagging alerts for further investigation. Addressing regulatory concerns, the tool incorporates a feature called 'explainability,' offering a comprehensive overview of the risk indicators, facilitating better decision-making processes. 4Institute of International Finance 5Anti Money Laundering AI | Google Cloud wns.com 03

5. AI / ML Solutions to Mitigate Financial Crime and Ensure Compliance Drawing on our extensive experience in these technologies, we are closely collaborating with clients to harness the power of AI / ML-based solutions across five pivotal use cases within the realm of financial crime and compliance. These include: AI / ML-based Transformation Solution AI / ML to optimize false positives and reduce human effort 2 AI / ML for adverse media screening and identification of true positives 4 1 3 5 Advanced Analytics / AI models for identifying unknown patterns, Link Analysis / early warning signals Intelligent AI-powered Workflow for KYC to improve customer experience and onboarding / reviews Generative AI to automate the narrative writing efforts in AML and EDD operations Figure 3: Five AI / ML Use Cases in Financial Crime Compliance wns.com 04

6. Automated Narration Advanced analytics and AI / ML models facilitate the automated narration of investigation outcomes by aggregating data, providing a holistic view of the customer's KYC profile. The system then delves into various aspects, such as transaction history, account details, customer relationships and significant counterparties involved in transactions. The automated narration analyzes transaction patterns and customer-counterparty relationships and identifies any anomalies present in these transactions. Client Lifecycle Management & Workflow AI-powered workflow orchestration can be a game-changer, offering valuable features such as seamless digital onboarding, automated Identity and Verification (ID&V), and self-service data input channels. Integration of document sourcing from public or third-party sources, along with data extraction capabilities, significantly accelerates the onboarding process and streamlines investigations. Screening and Investigation AI / ML models conduct a comprehensive analysis, comparing internal and external data to investigate hits. Advanced NLP programming and sentiment analysis scrutinize hits, assessing relevancy and derogatory information, ensuring thorough evaluation of potential risks. Link Analysis By harnessing the wealth of intelligence contained within historical data and transactions and conducting open searches, the system adeptly identifies unexplained relationships and establishes connections (Link Analysis) between individuals and entities. This capability empowers complex investigations and meticulous reviews, facilitating the discovery of hidden patterns and providing early warnings to prevent potential instances of fraud and money laundering. As a result, this approach enhances overall security and elevates risk management measures to safeguard against financial crimes. False Positive Reduction A self-learning ML algorithm analyzes AML platform alerts, triaging them as high, medium or low based on significance. This prioritization system distinguishes low-risk false alerts, effectively suppressing them / recommending threshold tuning to quell false alerts. wns.com 05

7. Embracing a Resilient Future The potential of AI / ML in revolutionizing financial crime compliance is undeniable. By harnessing the power of advanced technologies, financial institutions can proactively address regulatory challenges, detect complex fraud and money laundering patterns, and enhance their risk management strategies. AI / ML models offer unprecedented insights from vast data sources, enabling efficient and accurate decision-making while reducing false alerts and operational costs. Embracing AI / ML becomes imperative for institutions seeking to stay ahead in the fight against financial crime while fostering trust, integrity and resilience in the global financial ecosystem. Click here to learn how WNS is collaborating with global financial institutions and FinTech companies to tackle financial crime challenges and meet regulatory requirements. wns.com 06

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