The field of Artificial Intelligence (AI) traces its roots back to Alan Turing, who wrote a paper about thinking machines in 1950. Although the field is rapidly advancing, it's still really in its infancy. Over the last few years, Google CEO, Sundar Pitchai has been speaking about the increasing role of AI in software and it seems like this year might be the inflection point for the field. Companies as diverse as Google, with its "AI-first" approach, and Tesla, which created an OpenAI project, demonstrate the significant interest in the field.
Many banks, which are always looking for better ways to analyze and monetize huge data sets, are starting to adopt AI and machine-learning technologies for numerous use cases:
1. AI and machine-learning can really impact KYC compliance process in helping identify high-risk customers who need to be screened with an Enhanced Due Diligence (EDD) process. Using pattern recognition to explore associations among different types of objects helps EDD processes operate more efficiently. These methods are crucial in assisting human investigators in comprehending complex webs of evidence and drawing conclusions that are not apparent from any single piece of information. These methods are equally useful for creating variables that can be combined with structured data sources to improve automated decision-making processes. Typically, linkage data is modeled as a graph, with nodes representing entities of interest and links representing relationships or transactions along with dubious jurisdictions, companies, ultimate beneficial ownership (UBOs).
2. AI bots are really useful to perform repetitive tasks. Using chat bots to communicate with customers, analyzing their responses using Natural Language Processing (NLP), can critically save time and staffing needs to run KYC process.
3. AIs that monitor regulatory changes are able to identify information gaps and generate alerts to improve KYC completion. Cognitive engines now available can understand and analyze high volumes of regulatory changes and verify that a business is alerted to the most up-to-date policies. The use of AI – particularly natural language understanding (NLU), a subset of natural language processing (NLP) can select specific rules in lengthy regulatory documents and send them to people and departments that need to ensure compliance. NLP systems can also analyze documents to find topics that are involved in regulatory changes.
4. A joint Dow Jones Risk & Compliance and Association of Certified Anti-Money Laundering Specialists (ACAMS) survey reveals that half of the alerts generated in screening are false positives. As a result, and to lower the number of false positives, most of the major banks across the globe are shifting from rule-based software systems to AI-based systems, which are more powerful and adaptive when it comes to identifying a wide range of patterns in potential money-laundering transactions. . Recently many Financial Institutions have implemented Anti-Financial Crime Solutions, which uses unsupervised Bayesian learning techniques to understand customer behavior, which is further used to drive investigations and possible SAR filings. AI utilizing complex fuzzy logic and smart agents greatly reduce false positives.
5. Automation of SAR filings, report generation and visualization technologies to make sense of large volume of unstructured data can all be delivering using simple ML techniques.
6. One of AI’s biggest advantages will revolve around delivering workflow automation. AI can be used in generating documents, reports, audit trails and notifications. For instance, AI-based workflow automation reports generate risk profiles on both companies and individuals in just minutes, providing comprehensive and in-depth global due diligence information. Also, the reports provide links to the data sources, enabling them to be fully auditable, vital details for internal audit teams and regulatory examiners who typically want to know the accuracy, veracity and origin of any information used in AML decision-making. This capability is even more critical as recent and upcoming changes to global KYC regulations will require the identification of and due diligence on beneficial owners.
7. Analyzing the transactions that lead to UBOs via Link Analysis provides an unparalleled depth of information.
8. In the KYC/AML processes, adopting a risk-based approach is the best method. There are alternative sources that can assist in risk assessments such as email history, mobile data and mobile app analytics.
9. Cross-enterprise compliance, across a FI or bank’s various geographies, is currently a challenge. An AI-powered automated workflow would make it seamless to deliver enterprise-wide systems and processes. These forward-thinking solutions provide end-to-end program coverage to identify pseudo-client, intermediary, and internal FI risks, while simultaneously identifying false negatives and false positives with a feedback mechanism to improve the existing models and rules. Additionally, AI is assisting FIs to automate significant portions of the investigative process; allowing AML investigators to focus their attention on otherwise unintentionally overlooked red flags or suspicious account activity. The end result is a finely tuned cross-enterprise compliance program that operates more cost efficiently, and more importantly, functions more effectively than traditional systems.
10. Last, but not the least, KYC is all about truly knowing the identity of a customer, his or her risk profile and put them into relevant customer buckets to perform due diligence. It is also about keeping up with expanding regulations, to avoid penalties. AI techniques can help build better risk models for more accurate customer risk scores.
As big believers of digital KYC or eKYC, Trulioo’s electronic identity verification service is a shining example to numerous financial institutions on how to control KYC costs and complexity. The rules and regulations of KYC are not getting easier; only through digital KYC can companies attain the control they require.