Artificial intelligence (AI) remains a very hot technology across most industries, with analyst firms such as Gartner predicting the number of AI projects continuing to increase within the next year. In financial services, AI has been most commonly used to assist with risk analysis. Financial professionals and banking leaders continue to look to AI and other digital tools to streamline work and better leverage their customers’ data and address regulatory requirements that are specific to their industry. In 2019 Finextra and Pega surveyed senior business and technology managers from 54 financial and FinTech organizations around the world on the impact of AI on KYC, Client Lifecycle Management, and onboarding.

The top three ways in which AI can help onboarding are:

Supporting speed to market for services and products: Intelligently automating the execution of business rules and processes to complete onboarding more swiftly. AI can identify and route work automatically, adding both speed and accuracy to the process. For work that requires human review or judgement, AI can be used to recognize and route it to the correct persons.

Customizing offers for products and services to customers: Regulations surrounding financial product offers make it imperative that banks avoid any actions that could be construed as predatory. Applying AI to the monitoring and analysis of customer behavior, lifecycle patterns, and risk profiling can help banks determine suitability of new product offers.

Improved fraud and AML/KYC risk management: Risk models can use AI and machine learning to filter and assess data from a number of sources to detect patterns that might indicate money laundering or fraud then immediately alert to investigative teams of potential issues.

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How Customer Onboarding is being applied is explained below:

Customer Risk Scoring

Before onboarding customers, banks and non-banks can use AI to assess AML risk related to them based on a number of factors such as occupation, income sources, and the banking products used. They conduct customer due diligence and monitor the risk ratings throughout a customer’s lifecycle to make informed decisions on potential money laundering cases. Banks usually do an identity verification and risk assessment for their individual and corporate customers by collecting various details about them. The process is to ensure that they are not doing business with people or institutions involved in financial crimes such as money laundering and terrorist financing. Banks collect as much data as they can about their customers, analyse the data they obtained, determine their risk and provide a risk rating. Customers with a high risk rating are closely monitored for their actions. Low-risk customers are also monitored but not as diligently as high-risk customers. Even after onboarding a customer, banks periodically update their database about customers. Typically, they do data updates for high-risk customers more frequently than low-risk customers.

Why Current Methods Don’t Work: Pitfalls of Current Customer Risk Rating Models

Many of the current customer risk rating models are not robust to capture the complexities of modern-day customer risk management. Customer risk ratings are either carried out manually or are based on models that use a limited set of pre-defined risk parameters. This leads to inadequate coverage of risk factors which vary in number and weightage from customer to customer. Further, the information for most of these risk parameters is static and collected when an account is opened. Often, information about customers is not updated in the required format and frequency. The current models do not consider all the touchpoints of a customer’s activity map and inaccurately score customers, failing to detect some high-risk customers and often misclassifying thousands of low-risk customers as high risk. Misclassification of customer risk leads to unnecessary case reviews, resulting in high costs and customer dissatisfaction. Adding to this, the static nature of the risk parameters fails to capture the changing behaviour of customers and dynamically adjust the risk ratings, exposing financial institutions to emerging threats.

The AI Way of Customer Risk Scoring

Today, modern technologies like AI and machine learning are getting widespread attention for their ability to improve business processes and regulators are encouraging banks to adopt innovative approaches to combat money laundering. In the area of customer risk scoring, the need of the hour is a sophisticated technology that can capture the complete customer activity through proper identification of risk indicators and continuously update customer profiles as underlying activities change.

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All said, the key benefits of AI-based Customer Onboarding are:

Broader risk coverage: AI-based analytical tools assess risk across a comprehensive range of risk indicators that provides a 360-degree view of AML Risk relative to the customer, their relationships and activities. These dimensions are Customer, Counterparty, Transactions and Network Relationships.

Dynamic customer assessment: Customer assessment adapts over time to actual customer behaviour. This vastly reduces false signals and improves inappropriate behaviour detection.

Solution level agility: The solution is not a single “model.” AI tools can be specially designed with advanced ongoing self-learning to evolve based on what is happening within specific client portfolios, business policies and industry trends. This functionality is controlled by client configuration to support all model governance policy and regulation requirements.

Accelerated risk assessment: AI can help filter and present the most critical information needed for investigators to make effective risk-based decisions timely and consistently. The solution simplifies highly complex machine learning decisions into understandable and actionable information.

Reduced cost of compliance and reputational risk: AI solutions help identify high-risk customers and enable banks to take proactive measures to mitigate the risk of financial loss due to penalties along with various other regulatory, legal and reputational risks.