33. What is a Risk-Based Approach in AML?

A Risk-Based Approach (RBA) is a method where financial institutions identify, assess, and prioritize money laundering and terrorist financing risks and allocate compliance resources accordingly.

It is strongly recommended by the Financial Action Task Force (FATF).

34. Why is RBA important in AML compliance?

RBA is important because:

• It ensures efficient use of compliance resources

• Focuses more on high-risk customers and transactions

• Reduces unnecessary burden on low-risk customers

• Enhances regulatory compliance

35. What are the key components of a Risk-Based Approach?

1. Risk Identification

2. Risk Assessment

3. Risk Categorization (Low/Medium/High)

4. Risk Mitigation

5. Ongoing Monitoring & Review

36. What are the major risk categories in AML?

• Customer Risk

• Geographic Risk

• Product/Service Risk

• Channel Risk

37. How do you perform a Customer Risk Assessment?

Customer risk assessment includes:

• Nature of business

• Source of funds

• Politically Exposed Person (PEP) status

• Adverse media screening

• Country of residence

• Transaction behavior

Example: A PEP from a high-risk jurisdiction would be classified as high risk and subject to Enhanced Due Diligence (EDD).

38. What is Enhanced Due Diligence (EDD)?

EDD is additional scrutiny applied to high-risk customers. It includes:

• Detailed source of wealth verification

• Senior management approval

• Increased transaction monitoring

• Periodic review at shorter intervals

39. How does FATF influence RBA globally?

The Financial Action Task Force issues 40 Recommendations that require countries and financial institutions to adopt a risk-based AML/CFT framework.

Countries implement these recommendations into local laws.

40. How is RBA implemented in India?

In India, RBA is implemented under:

• Prevention of Money Laundering Act, 2002

• Guidelines issued by Reserve Bank of India

• Securities and Exchange Board of India

These regulators mandate customer risk profiling and ongoing monitoring.

41.How is AI used in AML monitoring?

Artificial Intelligence (AI) enhances AML monitoring by improving detection accuracy and reducing manual workload.

Key Uses:

• Transaction Monitoring: AI detects unusual transaction patterns beyond static rule-based systems.

• Behavioral Analytics: Learns customer behavior and flags deviations.

• Name Screening: Improves matching against sanctions lists (e.g., fuzzy matching).

• Risk Scoring: Dynamically updates customer risk profiles.

• Alert Prioritization: Predicts which alerts are high-risk.

  Example:

  Banks use Machine Learning models to identify mule accounts or layering activities in real time.

   

42.What are false positives in transaction monitoring?

A false positive occurs when a legitimate transaction is incorrectly flagged as suspicious.

Example:

A customer making a large foreign payment for education gets flagged as suspicious, even though it is legitimate.

Why it matters:

• Increases compliance workload

• Wastes investigation time

• Impacts customer experience

AI and risk-based approaches help reduce false positives.

43. How does AML apply to cryptocurrency?

Cryptocurrency transactions are subject to AML regulations to prevent misuse for money laundering or terrorism financing.

Regulatory Framework:

• Global standards by Financial Action Task Force (FATF)

• In India, governed under Prevention of Money Laundering Act (PMLA)

• U.S. oversight under Bank Secrecy Act

AML Measures in Crypto:

• KYC for crypto exchanges

• Transaction monitoring

• Travel Rule compliance

• Wallet screening

Crypto exchanges must verify users and report suspicious transactions just like banks.

44. What are AML risks in fintech?

Fintech companies face unique AML risks due to digital onboarding and fast transactions.

Major Risks:

• Remote onboarding fraud

• Identity theft

• Mule accounts

• Cross-border instant payments

• API integrations with third parties

Because fintech operates digitally, robust monitoring and e-KYC are critical.

45.What is e-KYC?

Electronic Know Your Customer (e-KYC) is digital identity verification without physical paperwork.

In India:

e-KYC is enabled through Aadhaar-based authentication regulated by Unique Identification Authority of India (UIDAI).

Methods:

• OTP-based verification

• Biometric authentication

• Digital document upload

It reduces onboarding time and improves compliance efficiency.

46. What is Video KYC?

Video KYC (V-CIP in India) is live video-based customer verification.

Process:

• Live video interaction

• Face match with ID

• Geo-tagging

• Liveness detection

In India, it is permitted under guidelines by Reserve Bank of India (RBI).

It enables secure remote onboarding while preventing impersonation.

47. How does automation improve compliance?

Automation improves compliance by:

• Reducing manual errors

• Speeding up screening

• Real-time monitoring

• Auto-generating regulatory reports

• Reducing operational costs

Example: Automated SAR/STR report generation for regulators.

It allows compliance teams to focus on high-risk cases instead of repetitive tasks.

48. What are RegTech solutions?

RegTech (Regulatory Technology) refers to technology solutions designed to help companies comply with regulations efficiently.

Examples:

• Automated KYC platforms

• Sanctions screening tools

• AI-based transaction monitoring

• Regulatory reporting software

RegTech helps financial institutions meet standards set by regulators like FATF, RBI, and global AML authorities.

49. How would you design a risk scoring model?

A basic risk scoring model includes:

Customers are scored and categorized:

• 0–30 = Low Risk

• 31–70 = Medium Risk

• 71–100 = High Risk

50. What are challenges in implementing RBA?

• Inconsistent data quality

• Over-classification of high-risk customers

• Lack of automation

• Regulatory changes

• False positives in monitoring systems

51. How does AI enhance traditional rule-based AML systems?

Traditional systems rely on static thresholds (e.g., alert if >$10k), which often miss complex crimes or generate too many false positives. AI uses machine learning to analyze massive, diverse data sets for, complex anomalies, patterns, and behavioral changes, significantly improving detection accuracy while reducing manual review time.

52. What is the role of Machine Learning (ML) in Transaction Monitoring?

ML models learn from historical data to identify complex, non-linear patterns of money laundering, such as structuring or unusual velocity. Unlike static rules, these models adapt to new criminal behaviors, reducing false positives by refining what constitutes “suspicious” behavior.

53. How can RPA (Robotic Process Automation) assist in KYC/AML processes?

RPA automates repetitive, rule-based tasks such as gathering customer information, screening against sanctions lists, or pulling data from adverse media sources. This increases operational efficiency, reduces manual data entry errors, and allows analysts to focus on investigation.

54. What is a “False Positive” and how can AI help reduce it?

A false positive occurs when legitimate activity is incorrectly flagged as suspicious by the system. AI reduces this by using predictive analytics and behavioral modeling to better understand a customer’s normal activity, differentiating it from actual illicit behavior.

55. What are the risks of relying solely on AI for AML?

Relying solely on AI creates “black box” risks, where it is unclear why a decision was made, leading to potential regulatory scrutiny. Furthermore, AI might fail to recognize new types of crime not present in its training data. Human oversight is essential to validate AI findings and ensure compliance.

Core AML/KYC Concepts to Combine with Technology

Three Stages of Money Laundering: Placement, Layering, and Integration.

Transaction Monitoring (TM): The process of detecting suspicious activity using automated, technology-driven, and manual reviews.

KYC (Know Your Customer) & CDD/EDD: Customer Due Diligence/Enhanced Due Diligence to understand the risk associated with a client.

Suspicious Activity Report (SAR/STR): Reporting suspicious transactions to authorities.