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Tapping into the Power of
AI & ML to Combat
Financial Crime
Neelesh Pal
Corporate Vice President,
Banking and Financial Services
Shanmugaraj Rajamani
Director – Solutions,
Financial Crime
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
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.
01
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1
CELENT
2
Financial Crime News
Figure 1: Fines Resulting from Violations
Highest AML Fines -
21st
Century (in US$ billions)
0.913
Westpac Goldman
Sachs*
HSBC JP Morgan ING
1.6
7.2
2.6
0.9
Highest Sanctions Fines -
21st
Century (in US$ billions)
Source: Financial Crime News
Unicredit BNP
Paribas
Stan Chart* Commerzbank Credit
Agricole
0.611
1.62
8.9
0.787
1.45
*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.
Longer Onboarding /
Investigation Timelines
High manual effort and
process inefficiencies lead
to delay in onboarding /
investigation
Data Quality Issues
Poor-quality data leads to
multiple challenges –
high false positive, failure
to flag true risk, excessive
manual effort and more
Multiple Siloed Systems
Banks have legacy
systems and multiple
downstream systems,
leading to high lead time
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.
Challenges in Financial
Crime Compliance
Frequent Regulatory
Changes
Need to adapt to
evolving regulatory
changes
High False Positive
Investigating excessive
false positives results in
significant labor costs
Lack of Skilled Resources
Meeting the escalating
demands in the FinCrime
space is a challenge due
to the scarcity of skilled
resources
3
Thomson Reuters
Some of the common challenges financial institutions face include:
Key Challenges
Figure 2: Key Financial Crime Compliance Challenges
02
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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
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.
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
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.
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
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.
The Transformative Potential
of AI & ML
4
Institute of International Finance
5
Anti Money Laundering AI | Google Cloud
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 Solutions to Mitigate
Financial Crime and Ensure
Compliance
04
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Figure 3: Five AI / ML Use Cases in Financial Crime Compliance
AI / ML to optimize
false positives and
reduce human effort
AI / ML for adverse media
screening and identification
of true positives
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
5
3
1
2 4
AI / ML-based Transformation Solution
05
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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.
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.
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.
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.
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.
06
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Click here to learn how WNS is collaborating with global financial institutions and FinTech companies
to tackle financial crime challenges and meet regulatory requirements.
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.
Embracing a Resilient Future
with WNS
Co-create to
outperform
Copyright © 2023 WNS (Holdings) Ltd. All rights reserved.
To know more, write to us at marketing@wns.com
or visit us at www.wns.com
About WNS
WNS (Holdings) Limited (NYSE: WNS) is a leading Business
Process Management (BPM) company. WNS combines deep
industry knowledge with technology, analytics, and process
expertise to co-create innovative, digitally led transformational
solutions with over 400 clients across various industries.
WNS delivers an entire spectrum of BPM solutions including
industry-specific offerings, customer experience services,
finance and accounting, human resources, procurement, and
research and analytics to re-imagine the digital future of
businesses. As of June 30, 2023, WNS had 59,871 professionals
across 66 delivery centers worldwide including facilities in
Canada, China, Costa Rica, India, Malaysia, the Philippines,
Poland, Romania, South Africa, Sri Lanka, Turkey, the United
Kingdom, and the United States.

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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 Shanmugaraj Rajamani Director – Solutions, Financial Crime
  • 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 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. 01 wns.com 1 CELENT 2 Financial Crime News Figure 1: Fines Resulting from Violations Highest AML Fines - 21st Century (in US$ billions) 0.913 Westpac Goldman Sachs* HSBC JP Morgan ING 1.6 7.2 2.6 0.9 Highest Sanctions Fines - 21st Century (in US$ billions) Source: Financial Crime News Unicredit BNP Paribas Stan Chart* Commerzbank Credit Agricole 0.611 1.62 8.9 0.787 1.45 *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.
  • 3. Longer Onboarding / Investigation Timelines High manual effort and process inefficiencies lead to delay in onboarding / investigation Data Quality Issues Poor-quality data leads to multiple challenges – high false positive, failure to flag true risk, excessive manual effort and more Multiple Siloed Systems Banks have legacy systems and multiple downstream systems, leading to high lead time 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. Challenges in Financial Crime Compliance Frequent Regulatory Changes Need to adapt to evolving regulatory changes High False Positive Investigating excessive false positives results in significant labor costs Lack of Skilled Resources Meeting the escalating demands in the FinCrime space is a challenge due to the scarcity of skilled resources 3 Thomson Reuters Some of the common challenges financial institutions face include: Key Challenges Figure 2: Key Financial Crime Compliance Challenges 02 wns.com
  • 4. 03 wns.com 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 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. 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 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. 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 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. The Transformative Potential of AI & ML 4 Institute of International Finance 5 Anti Money Laundering AI | Google Cloud
  • 5. 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 Solutions to Mitigate Financial Crime and Ensure Compliance 04 wns.com Figure 3: Five AI / ML Use Cases in Financial Crime Compliance AI / ML to optimize false positives and reduce human effort AI / ML for adverse media screening and identification of true positives 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 5 3 1 2 4 AI / ML-based Transformation Solution
  • 6. 05 wns.com 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. 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. 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. 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. 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.
  • 7. 06 wns.com Click here to learn how WNS is collaborating with global financial institutions and FinTech companies to tackle financial crime challenges and meet regulatory requirements. 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. Embracing a Resilient Future
  • 8. with WNS Co-create to outperform Copyright © 2023 WNS (Holdings) Ltd. All rights reserved. To know more, write to us at marketing@wns.com or visit us at www.wns.com About WNS WNS (Holdings) Limited (NYSE: WNS) is a leading Business Process Management (BPM) company. WNS combines deep industry knowledge with technology, analytics, and process expertise to co-create innovative, digitally led transformational solutions with over 400 clients across various industries. WNS delivers an entire spectrum of BPM solutions including industry-specific offerings, customer experience services, finance and accounting, human resources, procurement, and research and analytics to re-imagine the digital future of businesses. As of June 30, 2023, WNS had 59,871 professionals across 66 delivery centers worldwide including facilities in Canada, China, Costa Rica, India, Malaysia, the Philippines, Poland, Romania, South Africa, Sri Lanka, Turkey, the United Kingdom, and the United States.