Know-Your-Customer (KYC) is the epitome of customer identification. What if KYC were not viewed through a compliance lens only? KYC has many more capabilities. Financial companies cannot avoid the cost of KYC. They must identify new customers in the onboarding process in a compliant manner. For the basic record, they verify the data of natural persons on the basis of official documents. The second step is comparing personal data with sanctions and PEP lists. Depending on the type of client, additional information is provided on domicile, nationality, business activity, and assets. These parameters are used to create the KYC risk profile, establishing the basis for identity verification in compliance with the risk-based approach. As an existing customer, the person is subject to regular screening in order to identify changes in business behavior or in the sanction and PEP status.
The latest trend: Using KYC data to increase profit
Creative finance companies are increasingly recognizing that KYC data offers more than just compliance functionality. They are using KYC to increase profitability and to provide valuable insights for product development, marketing, and sales.
Practicing data protection
Compliance with data protection regulations is important in this context. Personal data may be collected, processed, and used for specific purposes only. Use for a non-stipulated purpose is permissible if the person affected consents or a legal requirement permits. However, the GDPR (General Data Protection Regulation) also recognizes that further processing for statistical, scientific, and historical purposes does not conflict with the principle of specific use. To pursue these ends, personal data that has been collected for another purpose is analyzed anonymously or after being pseudonymized.
From the KYC basic record to the 360-degree profile
Google’s ability to ensure that a taxi is waiting for us before we even realize that we need it speaks for intelligent data analysis. In the realm of customer relationships, this means that the more individually a customer is to be served, the more data must be collected and analyzed. This includes more than just information on financial background, risk affinity, and personal preferences for certain investment products. The interesting data is the “movement data”. This data addresses the following questions:
- What business and private interests does the customer have?
- Who is his employer?
- What does he do with information?
- How is he networked?
- Who are his influencers?
- Is he likely to quit?
Machine learning as a supplementary factor
Machine learning in the area of compliance means making better decisions via more accurate data analysis. Models for compliance testing are acquired automatically with machine learning. The goal is greater efficiency, achieved by improving the false positive rate, for instance. However, the speed with which compliance analysts respond to threats is also important – detecting new fraud patterns at an early stage, identifying new methods of money laundering immediately and including them in the monitoring, etc.
But machine learning also influences sales. Smart data allows predictive analysis, which literally sends the taxi to the customer before he knows that he needs it. In other words, machine learning can make customers better offers and provide them with a better customer experience and better service – perhaps even before they know that they want it.