Intelligent segmentation for KYC
The proliferation of decentralized fintech solutions such as digital wallets and crypto-projects have created a myriad of channels across which people transact on a day-to-day basis, making it hard to monitor customer behaviour, and construct anticipated activity profiles for AML compliance.
In such rapidly changing tides of today’s digital market, banks and FIs can no longer afford to merely avoid risk and lose out on the rewards from potential businesses, without a continuous and proactive KYC process. It is therefore imperative that FIs adopt a growth focused approach towards AML compliance, with an effective customer due diligence and risk management methodology to thoroughly vet business users.
An intelligent customer segmentation model with well defined clusters/profiles is useful here as it helps predict customer behaviour more accurately with precise thresholds for detecting unusual activity and preventing fraudulent transactions. It also improves the performance of your AML service by reducing the number of false positives alerts.
A few foundational guidelines
- Segmentation for KYC is not a one-off activity. Customer activity needs to be continuously monitored and any unprecedented change should trigger alerts and be accurately captured in the customer profile. A sound model is therefore dynamic with decision trees that automatically classify, update, and re-classify groups/profiles on a periodic basis, as and when the necessary conditions are met.
- Case/group specific questionnaires derived from relevant business attributes and behavior patterns should take the place of static KYC checklists for effective CDD. Clustering customer groups across segments based on chosen attributes further helps compare and identify relationships, and patterns in behaviour data for improved analytics.
- An expansive segmentation model includes attributes such as past transaction types and currencies in addition to transaction volumes and amounts, to provide a more holistic view of the customer. Profiles adjusted to include investment portfolios and other financial footprints besides transactional activity reveal the finer details within the larger picture.
- By accounting for market changes while determining risk scores, FIs can garner deeper insights into what’s really unfolding, and fine tune their segmentation models.
The final word
To gather this 360 degree view of the customer, FIs need to assimilate data from a labyrinth of disparate systems and interfaces across organizational silos and third parties. A KYC process built on AI and ML automates this cumbersome process simplifying activity monitoring and case management.
AML services with self-modeling capabilities built on un-supervised learning can thus help identify high risk customers, localize suspicious activity, and pre-empt fraudulent transactions.
