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AI, KYC, and Bank Automation

AI and Automation: 10-point guide to its impact on KYC Process in Banks

AI, KYC, and Bank Automation

The term Artificial Intelligence (AI) has been around for a while. A quick search on the web reveals that the field of modern AI was born in the year 1950, when Alan Turing published a paper on thinking machines. Here we are, almost seven decades later, still in the advent of this emerging technology. 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. In May 2017, Pichai explained how at Google, it is an “AI-first” approach for several of its products. Tesla’s Elon Musk has created OpenAI project, a nonprofit organisation to conduct cutting edge research on the subject.

The banking industry is always quick to adopt new technologies. In the next few years, we expect an “AI-first” approach in many banks across the globe. Let’s examine how AI and machine-learning can potentially impact KYC/AML processes adopted by banks:

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. Based on pattern recognition techniques coupled with unstructured text analysis, it is made more efficient to identify relevant customers for EDD. AI-based link analysis is a set of techniques for exploring associations among large numbers of objects of different types. 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 are even trained to understand ever evolving regulatory changes. They are used to identify gaps in collection the customer information and generate alerts for KYC process 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 analyze documents to identify people, products and processes affected by legal and 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 robust and intelligent to uncover a broader array of potentially illicit AML transaction monitoring patterns. 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. Link Analysis, a popular methodology in AI connects various transactions leading to the UBO is a game changer in this space.

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 the growing regulations, to avoid the big penalties, as institutions have learnt the hard way. AI techniques, especially around Bayesian frameworks can help build more accurate risk scores for customers.

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.

Deepak Amirtha Raj is a Strategy & Research Analyst in the Risk and Compliance sector. He focuses on Business Strategy Research, Emerging Technologies and Advanced Analytics. His deep understanding of the industry combined with his thoughtful and strategic approach has helped many RegTech players and Financial Institutions. He is a motivator and coach combining business acumen with analytical depth to align operational efficiencies with corporate goals. Deepak had previously worked with Royal Bank of Scotland.

The information in this blog is intended for public discussion and educational purposes only. It does not constitute legal advice.

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