The Evolution of KYC: Shaping a Dynamic Customer Risk Profile with Machine Learning
In an era of proliferating financial crime, KYC (know your customer) measures have become more vital than ever. However, traditional static-parameter-based KYC processes alone are falling short in the face of sophisticated financial crimes. As a result, a more dynamic, data-driven risk assessment approach is required.
Machine learning (ML) is ushering in this transformative change in KYC operations. ML-based softwareallows financial institutions to evolve KYC, shifting from static to dynamic risk assessment. At SymphonyAI, we’re focused on addressing three key use cases.
- Integrating ML behaviour score – a risk-based approach in which ML scores form a subset of overall customer risk
- Implementing a full machine-learned score – a full replacement to or a side-by-side augmentation of parameter-based customer risk
- Applying ML to risk contribution elements – using ML to generate, for example, geographic, product or occupation risk level risk to score customers against.
1: Sharpen detections with an integrated machine learned behaviour score
The first key application of ML in KYC involves integrating a machine-learned behaviour score into the customer risk profile. This score, derived from transactional behaviours, provides a dynamic risk assessment to help identify irregularities that may escape static parameters.
Traditional KYC might flag high-value transactions as risky based on the transaction size alone. In contrast, a machine-learned behaviour score incorporates intricate patterns, such as changes in transaction frequency, transaction size, or transaction types.
By providing more nuanced, accurate risk profiles, this approach greatly improves detection efficiency. A 2019 study by Accenture revealed that 85% of banks using ML in risk assessment were able to detect financial crime better.
Incorporating transactional behaviours into risk assessments also aligns with the fifth EU Anti-Money Laundering Directive, which mandates more stringent scrutiny of transactional behaviours.
2: Drive down false positives by implementing a full machine learned score
The next ML application in KYC is creating a comprehensive machine-learned risk score, incorporating both structured data (e.g., transaction history, account information) and unstructured data (e.g., emails, notes, externally sourced data).
These models uncover hidden correlations and risk patterns across diverse data points. Due to their complexity, these machine-learned scores usually run in tandem with traditional parameter-based scores, so organizations can evaluate both outputs side-by-side until the machine-learned model proves its reliability.
Using these models can significantly streamline KYC processes by reducing manual checks and investigations. According to McKinsey, implementing ML can reduce false positives by up to 20%, leading to substantial time and resource savings.
3: Seamlessly adapt by applying machine learning to risk contribution elements
The third use case for ML in KYC involves applying ML algorithms to risk contribution elements, such as geographic risk. Here, an ML model generates a country risk score based on a variety of factors, which is then used to evaluate a customer’s geographic risk profile.
The strength of this approach lies in its adaptability. An ML model can dynamically update country risk scores based on geopolitical changes, economic indicators, and crime rates, offering a more nuanced, timely risk assessment than traditional methods.
As we enter the era of “Perpetual KYC,” which demands continuous customer monitoring, such dynamic risk assessments are increasingly essential.