Know Your Customer Programs and How AI-native MDM Helps

Editor’s Note: This post was originally published in August 2023. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

As a financial services or insurance provider, you’ve likely invested in Know Your Customer (KYC) programs to meet compliance and risk assessment requirements. After all, these programs are critical for identifying suspicious entities, preventing fraud, and adhering to stringent regulatory guidelines. However, many firms find that the effectiveness of their KYC programs is often constrained by slow, manual processes and fragmented data sources. To overcome these persistent challenges, financial services and insurance companies need a new approach to entity resolution using AI-native master data management (MDM). 

The Challenge: Extracting Meaningful Insights from Messy Data

Know Your Customer programs are not about achieving perfectly clean or accurate data; they are about assessing customer risk by identifying high-risk entities hidden deep within complex, disjointed, and often incomplete data. The primary goal is high recall—ensuring no suspicious entities slip through the cracks—while maintaining reasonable precision to minimize false positives that burden compliance teams with unnecessary manual reviews.

Many times, financial institutions rely on large teams of analysts to manually verify customer information. When onboarding a new client, compliance teams gather company firmographics, executive lists, and board member details, then ask analysts to manually cross-check these entities across fraud databases, sanctions lists, and news sources. Not only is this process tedious, labor-intensive, and prone to errors, but it often takes weeks, if not longer, to complete. As an example of this process, let’s take a look at a popular KYC application: fraud risk assessment.

Breaking the Data Unification Logjam

A global financial institution needs to continuously assess its customers to ensure compliance with regulations and mitigate potential risks. The goal of these assessments is twofold: 1) Verify that customers are who they claim to be (and not entities posing under similar names), and 2) Ensure customers are not associated with criminal activities, including money laundering, fraud, terrorism or any other reputational risk concerns.

KYC processes require linking internal customer data with various external sources such as sanctions lists, adverse media reports, politically exposed persons (PEP) databases, and other regulatory watchlists. However, the traditional approach to connecting this information together is manual and inefficient. Analysts spend significant time searching through disparate sources and comparing results, often leading to false positives and lengthy review cycles. And to build a comprehensive view of potential risks, financial institutions must undertake the following:

• Data Ingestion and Integration requires gathering customer-related data from the various data sources, including third-party data sources and internal systems and Continuously pulling in relevant external data sources such as sanctions lists, negative news, and regulatory reports to detect new risks in real time.
• Entity resolution accurately identifies and links related entities across disparate data sources, even when information is incomplete or presented differently, including matching names, addresses, corporate hierarchies, and associated individuals
• Validation and Review involves sifting through vast amounts of data and surfacing potential matches or clusters for human review to help with the decision-making process and allow compliance teams to review potential threats.

Sounds relatively simple, right?

Now, imagine that you have one million records—or more—and that your analysts are going through them on Excel spreadsheets. At this point, you have an n-squared problem on your hands that you can’t possibly address in a reasonable period of time. Not so simple at all. 

AI-native MDM: Automating KYC for Speed and Scalability‍

AI-native master data management (MDM) solutions enable financial institutions to streamline KYC processes and improve risk detection. Unlike time-consuming, manual processes, AI-native MDM emphasizes entity resolution and data integration across both internal and external sources.

Using AI-native MDM for Know Your Customer programs provides numerous benefits to financial services and insurance providers, including:

• Faster onboarding: AI accelerates entity matching, reducing the time needed to verify customers from weeks to days. Instead of waiting for analysts to manually validate entities, AI automates data linking and risk assessment in real time.
• Improved Risk Discovery: AI can identify hidden relationships between entities, detecting patterns of fraudulent behavior that human analysts might miss. And, it improves recall by uncovering indirect connections between suspicious individuals and organizations.
• Fewer false positives: While maximizing recall, AI also improves precision, minimizing unnecessary alerts that would otherwise require manual investigation. This reduces compliance workload while ensuring legitimate threats are identified.
• Real-Time Risk Identification: AI integrates new external data sources in real time, helping financial institutions maintain up-to-date customer profiles with the latest available information. This allows compliance teams to identify emerging threats and suspicious entities as soon as they surface, rather than waiting for periodic manual reviews, and ensures they remain compliant with evolving regulatory requirements and industry standards.

AI vs. Manual Processes: A Transformative Shift

Historically, KYC processes relied on human analysts to sift through fragmented data manually. For example, a relationship manager might send a compliance team a list of customer executives and board members. Analysts would then spend weeks manually searching for matches in regulatory databases, often encountering false positives that required repeated back-and-forth validation.

AI-native MDM automates these tasks. AI extracts and matches relevant information across various sources, presenting compliance teams with high-confidence results for review rather than raw data that requires them to invest extensive manual effort. This shift allows financial institutions to reduce compliance costs, speed up customer onboarding, and improve risk detection accuracy.

AI-native MDM capabilities transform KYC by providing:

• Entity resolution & relationship mapping: AI links customer data across different sources, uncovering complex hierarchies and hidden associations.
• Automated data integration: AI pulls in real-time updates from external sources, ensuring KYC data remains current and comprehensive.
• Change detection & monitoring: AI continuously tracks changes in customer data, flagging any updates in regulatory status, sanctions lists, or adverse media coverage to help institutions stay ahead of risks.

The Future of KYC: AI-driven Efficiency

As regulatory scrutiny increases and financial criminals become more sophisticated, AI-native MDM provides a new, transformative way for financial institutions to manage KYC. By shifting from manual, labor-intensive processes to AI-driven automation, firms can improve compliance, reduce operational costs, and enhance risk detection capabilities.

Tamr’s AI-native MDM offers financial institutions a scalable, intelligent approach to KYC. Whether you need to streamline onboarding, improve risk monitoring, or enhance data integration, Tamr enables faster, more accurate, and more efficient KYC operations.

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