Reducing KYC data overload in corporate onboarding

To build comprehensive risk profiles for corporate clients, banks collect large amounts of data. Reducing KYC data overload is vital and requires the bank to focus on prioritizing relevant, accurate and consistent data for efficient and accurate KYC.

The issue of KYC data overload

While it may seem intuitive to collect as many data points as possible, this approach often leads to diminishing returns. When banks rely on multiple datapoints and providers to build a full view of a corporate client, they inevitably encounter conflicting versions of the same information.

These conflicts can range from inconsistencies in beneficial ownership data to differing business addresses or variations in company names. The result? An overwhelming amount of “white noise” that obscures essential insights, hinders efficient decision-making, and increases the risk of inaccurate risk assessments.

Why fewer, higher-quality data points are better

The solution to KYC data overload is not more data, but better data. Quality trumps quantity when it comes to building a clear, actionable profile of a corporate client. Focusing on fewer, higher-quality data points means banks can rely on information that is accurate, up-to-date, and directly relevant to assessing compliance risk.

To achieve this, banks should establish clear criteria for the types of data that contribute most directly to KYC. For instance, attributes such as legal entity identifiers, up-to-date beneficial ownership information, and verified business addresses. All provide a solid foundation for risk assessment, without creating data clutter.

Addressing conflicting data points

A key part of building a high-quality KYC profile involves managing the conflicting data points effectively. With information obtained from a variety of sources and databases; internal, external, public, and private, banks need a robust approach to handling these discrepancies.

An effective data and entity management strategy , together with data primacy rules, allows banks to reconcile the conflicting data points. Furthermore, creating a single, consolidated view of each corporate client.

This works by:

1. Standardizing data: Automation can normalize data into standardized fields for smooth data ingestion
2. Identifying data conflicts: Automation can detect discrepancies between data sources, such as mismatched addresses or variations in ownership information, flagging them for further review.
3. Applying hierarchy rules: Predefined rules identify which data sources to prioritize, according to a bank’s policy.
4. Entity resolution: Data points are joined together and merged to form a unified profile, ensuring consistency and reduced redundancies. This step also helps eliminate outdated information. Ensuring the final profile reflects the most recent and accurate view of the corporate client.

Ensuring freshness and accuracy for every onboarded entity

Maintaining accurate, up-to-date profiles is essential for effective ongoing due diligence. For banks, that means investing in technologies and processes that support real-time updates and data validation.

Additionally, leveraging external sources in a coordinated way is critical to keeping data accurate over time. By integrating public records, credit bureaus, and other trusted databases, banks can build and maintain a comprehensive view of each corporate client without excessive redundancy.

Practical steps for banks to enhance KYC data quality

To reduce data overload and improve KYC processes, banks can implement a few practical strategies:

1. Data relevance assessment: Regularly review data sources and attributes to ensure they directly contribute to risk assessment and compliance requirements. Eliminate data points that add complexity without clear value.
2. Centralized data management: Use a single platform to consolidate internal and external data sources, reducing the risk of conflicting information and simplifying access to critical information.
3. Automated data matching: Invest in software that can automatically match and merge data from various sources. Eliminating duplicate or outdated records while ensuring each profile is consistent and accurate.
4. Ongoing data quality monitoring: Establish data quality benchmarks and continuously monitor data sources to catch errors early and maintain high standards across all KYC profiles.

Automation – the key to accurate, real-time data

The future of KYC lies in automation, with perpetual KYC (pKYC) as the goal. To lay the groundwork for a pKYC model, financial institutions must first identify the most meaningful data points and sources. Those that directly drive risk variation. By focusing on fewer, higher-quality data points, banks can monitor critical indicators more effectively. In turn, helping to mitigate risk and streamline the journey toward pKYC.

Tier 1 banks spend an average of 4.5 times more on manual customer due diligence (CDD) and KYC processes than on technology solutions, underscoring a major opportunity to improve risk assessment effectiveness through advanced, data-focused strategies. Achieving this efficiency requires financial institutions to first clean, consolidate, and ensure the freshness of their data. By automating data collection and integration, banks can reduce manual workloads, improve data accuracy, and provide customers with smoother, more seamless experiences.

A shift towards quality-driven KYC

For banks, a quality-driven approach to KYC means focusing on fewer, more accurate data points that truly reflect each corporate client’s risk profile. By reducing KYC data overload and clutter as well as addressing conflicts through automated match and merge processes, banks can streamline onboarding, reduce compliance costs, and ultimately achieve better KYC outcomes.

In an industry where both compliance and client satisfaction are paramount, prioritizing data quality over sheer volume is not only more efficient but also more effective in building trust and managing risk.