customer due diligence services

Exploring the Role of AI and Machine Learning in Customer Due Diligence

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Have you ever installed an e-payment or Fintech mobile application? Can you start making online payments immediately after installing the mobile application? The answer is no because you must provide a few details to get started. You must provide your name, email ID, UPI ID, bank account number, and other details to set up the e-payment system. The provider or the Fintech company needs these details to confirm that you are a genuine user. Customer due diligence is essential and also a challenge for organizations, especially those offering financial services.

Let us explore the role of Artificial Intelligence (AI) and Machine Learning (ML) in customer due diligence. 

Demystifying Customer Due Diligence

Before discussing the role of AI and ML, it is essential to understand the concept and process of customer due diligence. If it weren’t important, there would not be a high number of third parties offering customer due diligence services. Customer due diligence is the process of collecting and analyzing information related to new and existing customers. Customer due diligence is essential for analyzing the potential risks associated with a customer. It is crucial to note that customers in the financial services sector can be individuals or organizations. Service providers must identify the risks associated with these customers before indulging in business with them.

Customer due diligence can occur in different sectors but is indispensable in the financial services sector. Due to strict regulations, financial services firms are compelled to indulge in customer due diligence. Corporate banks, investment banks, brokerage/trading firms, asset managers, commercial banks, and other entities actively indulge in customer due diligence. Let us understand the significance of customer due diligence with a real-life example. You know that there is a risk of money laundering for banks. How will a bank differentiate between a genuine customer and a money launderer? It can happen when the bank collects customer details and documents. The details/KYC documents of the customer are verified to determine their authenticity.

Must Read: Leveraged Lending: Evolution, Growth and Heightened Risk

Understanding the Role of AI and ML in Customer Due Diligence

Customer due diligence has changed over the years, mainly due to digitization. Financial services firms now depend on third-party customer due diligence services for increased innovation. The introduction of AI and ML has made customer due diligence more effective and robust. Here’s how AI and ML have impacted customer due diligence:

Enhanced Data Analytics

Collecting customer data is not the only task for financial institutions. Customer data is analyzed to generate meaningful insights. Traditional or manual data analytics techniques are time-consuming. Not to forget, they might not offer accurate results when the size of the data is increased. However, AI and ML have automated the data analytics process for customer due diligence. Also, large volumes of data can be analyzed with the help of AI and ML-led systems. A financial services firm does not have to hire a vast team of analysts to generate insights manually.

Enhanced Risk Scoring

Risk scoring is an integral part of customer due diligence. Every customer is assigned a risk score based on their details. When a customer’s risk score is high, the financial institution might deny offering them services. A high-risk score might also indicate that the customer is involved in unlawful activities. Generating risk scores manually has always been a headache for financial services firms. However, AI and ML have resolved these issues for financial services firms. Machine learning models are available to automate the risk-scoring process. Risk scores are assigned to customers based on transaction behavior, document accuracy, and other factors.

Identity and Document Verification

AI and ML models are being used for identity and document verification. AI-powered biometric systems are in place for fingerprint and facial recognition. Documents can be scanned and verified within seconds through AI and ML-led systems. These systems can analyze holograms, watermarks, and other features of documents to determine their authenticity.

Enhanced Compliance

The regulatory landscape for customer due diligence might change with time. As a result, organizations change their customer due diligence processes to avoid fines or penalties. AI and ML-led systems can be updated to match the current due diligence requirements. You don’t have to bring a major change in your due diligence processes, as automated systems are updated easily.

Customer Segmentation

AI and ML can help categorize customers into different sections based on their risk scores. You can put in extra effort for customers in the high-risk section. Customer profiling becomes easier when they are categorized based on their risk profile.

In a Nutshell

Besides the aforementioned, there are many other uses of AI and ML in customer due diligence. Financial services firms can rely on third parties offering customer due diligence services or support. These third parties can bring innovation to your due diligence process by introducing new-age technologies. It’s time to implement AI and ML for customer due diligence.

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