Spotlight on the Remarkable Potential of AI in KYC (Know Your Customer)

By Deepak Amirtha Raj, Researcher, Strategist, and Writer.

"Traditional rule-based KYC-AML technology necessitates significant dependence on manual efforts particularly in alert investigation stage, which is costly, error-prone, and inefficient"

The ultimate aim of any Financial Institution (FI) is to earn the confidence and faith of their customers but equally important to verify the information customers provide back to them. The regulators are increasingly concentrating on ensuring that banks have robust and effective controls in place for customer due diligence (CDD). Lapses in money laundering and CDD controls exposed several FIs which resulted in regulators tightening their levels of supervision on the industry as a whole

A few examples:

1) United Overseas Bank (UOB), which was fined $900,000, and Switzerland's Credit Suisse Group, which was fined $700,000 - both for breaches of anti-money laundering requirements and control lapses

2) The German lender Deutsche Bank has received a new $204 million fine by British regulator FCA for inadequate anti-money laundering controls

3) The Monetary Authority of Singapore (MAS) has withdrawn the merchant bank status of Switzerland-based Falcon Private Bank's Singapore branch for serious failures in anti-money laundering (AML) controls

For FIs, customer due diligence is a vital element to derisk and protect themselves against possible financial crimes
 

Driving Changes in KYC|AML

 
The KYC space faces few driving changes that must be taken into account before implementing CDD program aided by AI:

1) Ever Evolving Regulations - Regulatory environments such as FATCA, OFAC, 4AMLD, MAS guidelines etc. they vary across the globe considerably. Multinational banks need to ensure compliance not only in their home country but also in environments that are more complex and have fewer infrastructures.

2) More Regulatory Scrutiny - Not adhering to the laws has very punitive outcomes for the organization. For Example, Deloitte has highlighted in its “Meeting new expectation” report that AML sanctions-related fines and penalties imposed in 2013 and 2014 quadrupled the total for the previous nine years.

3) Cost Pressure - An average bank spends £40m a year on KYC Compliance, according to a recent Thomson Reuters Survey, which also revealed that some banks spend up to £300M annually on KYC (Know Your Customer) Compliance and Customer Due Diligence (CDD), this cost of compliance is increasing exponentially.

4) Legacy Systems - The KYC documents are retained over several years in document management systems, these are not always easy to locate.
 

Artificial Intelligence in KYC AML

 
Artificial Intelligence (AI) takes KYC and AML compliance to the next level. AI isn't just a technology, it is a collection of related technologies which has the potential to automate workflows and quickly analyze large volumes and different types of data. Some of the implied benefits of using Artificial Intelligence in KYC AML are:
 

1. Link Analysis:

 
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, UBOs.

“AI-enabled solutions can not only automate significant parts of operations but also offer superior insights through advanced capabilities for analyzing structured and unstructured data.”

2. Pattern recognition:

 
In most cases, money launderers hide their actions through a series of steps that make it look like money that came from illegal or unethical sources are earned legitimately. Most of the major banks across the globe are shifting from rule-based software systems to artificial intelligence based systems which are more robust and intelligent to the anti-money laundering patterns. Recently FICO has developed 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.

3. Unstructured Data Analysis:

 
AI in KYC relies more on Natural Language Processing (NLP) and supervised machine learning techniques. Each of these technologies has specific uses and NLP, in particular, is starting to come into widespread use in helping to analyze unstructured content such as adverse media. Together with machine learning, NLP-based AI can "read" such articles and perform a range of tasks including extracting metadata, identifying entities that are referred to, and "understanding" the intent or purpose of specific parts of the document.
 

4. Workflow Automation:

 
AI can also be used in generating documents, reports, audit trails and notifications. For instance, DDIQ’s 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, they are fully auditable. 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.

What do the AI providers think of their prospects? Naturally, they are optimistic about the potential to win bank clientele. "The catalyst for widespread adoption of AI for KYC/AML tasks will likely be the concern about competitive edge," says McLaughlin. As is the case with all new technology, once a few large players become interested, the rest fall in line.

Regardless of all the new technology, AML professional should not worry about losing their jobs anytime soon. There aren't enough qualified AML professional to go around. With regulators only too eager to penalize banks for any failures in KYC/AML compliance, institutions are staffing up. Mallinath Sengupta said,"Al doesn't replace human intelligence, but improve it". It's true, banks will continue to use analyst to make the ultimate decisions on whether a transaction is suspicious and must be reported, but they can be more productive and have more confidence that their decision is accurate.

 
Bio: Deepak Amirtha Raj is a researcher, strategist, and writer. He studied engineering and business at Saint Joseph's College and proceeded to work in a fast upscale environment, assuring client delight on luxury super yachts. He helps firms by teaching them about Artificial Intelligence, Augmented & Virtual reality, Machine learning and Big Data. He is optimistic about the potential it can bring into the world. He runs a local musicians studio with his brother and loves promoting live music events. You can find him on LinkedIn and Twitter.

Original. Reposted with permission.

Related:

• Link Analysis for Fraud Detection: How Linkurious enabled investigation of the massive Panama Papers leaks
• Data Science Dividends – A Gentle Introduction to Financial Data Analysis
• Unsupervised Investments: A Comprehensive Guide to AI Investors