The role of big data in KYC or Know Your Customer, is transforming the financial sector by enhancing customer due diligence, improving risk assessment accuracy, and enabling more proactive detection of fraudulent activities.
Big data is large volumes of unstructured or structured data-sets. The data sets are processed with digital or automated tools and then, using big data analytics approach, institutions may identify complex data patterns and extract valuable insights from data sets. Institutions may perform statistical analysis, and forecasting, to make AML risk assessments and take relevant customer onboarding decisions.
In complex or big data environments the financial institutions may use various special languages, techniques, tools, and a combination of them to develop and implement efficient and improved versions of identification, screening, and verification for AML/KYC compliance. Financial institutions may combine different techniques to avail desired compliance outcomes and improve the overall KYC compliance culture. Combining fuzzy matching techniques helps in performing more accurate KYC searches and screening, and provides a solution to big data, including more complex data sets, and data fields.
Use of advanced techniques such as grid-computing or in-memory data analytics institutions may use any volume or amount of big customer data to do customer risk assessment and profile analysis. Sometimes data is first structured, selecting only what is needed for risk assessment and customer profile analysis.
Increasingly, it is being applied to tasks within advanced data analytics, including artificial intelligence (AI). Typically, web scraping provides an easier and less expensive way to get customer data for KYC risk assessment and further use.
Institutions may perform real-time customers’ KYC risk assessments at the moment a customers create account. This allows for automated customer wise KYC risks identification, assessment and reporting.
The Role of Big Data in KYC
Big data affects the KYC compliance process. Regulatory authorities seek to assess and evaluate every step of the KYC process, including collection, processing, and storage of customers’ data. One of the main reasons for this is to perform in-depth KYC process operating effectiveness assessment.
To achieve KYC compliance status, an institution must develop digital security strategies and controls to protect the customer data. The analysis must demonstrate how each KYC risk mitigation strategy works and its level of operating effectiveness. This is where big data comes in and helps provide accurate and predictive risk reports on the likelihood of a cyberattack leading to loss of customers’ data. The process involves collecting all the data the institution has and does not have and analyzing the data using statistical algorithms to look for hidden or complex data patterns and identify anomalies to detect fraud, KYC policy violations, and other regulatory non-compliances.
There is a growing need among institutions to use big data and KYC technology to perform more cost-effective and real-time customer behaviour assessment at the time of onboarding and thereafter. For these reasons, there can be seen the upward trend in the market value of AML and KYC tools and software. In previous years, the market was valued at more than $650 million and $850 million. The AML/KYC tools and dsoftware market may be projected to reach around more than $1.70 billion.
Advanced data collection, analytics, and cognitive technologies like AI, ML, and automation are already helping institutions to reduce the risks of onboarding criminals and reduce false positives to improve overall compliance process and reduce compliance cost big data solutions help in:
- filtering out negative information during performing enhanced due diligence checks
- identifying and reporting suspicious transactions promptly
- a better understanding and real-time application of regulatory requirements
- reducing the number of false positives or false red-alerts during customer identification, screening, verification and financial transactions scrutiny/monitoring
- optimizing the process of AML/KYC risk assessment to assess ML/TF risks associated with potential or existing customers
- analyzing aggregate customer data or transactions, and segments across all lines of business activities, to ensure regulatory compliance.
Final Thoughts
Big data plays a pivotal role in enhancing KYC and AML compliance processes, allowing financial institutions to extract valuable insights from vast datasets. With advanced analytics and techniques like AI and ML, institutions can promptly identify and report suspicious activities, optimize risk assessments, and reduce false positives. Regulatory authorities intensively scrutinize every facet of the KYC process, emphasizing the importance of secure and effective data handling.
The burgeoning reliance on these advanced data tools is evident in the growing market value of AML/KYC software, projected to reach over $1.70 billion. As institutions seek real-time customer behavior assessments and streamlined onboarding processes, leveraging big data becomes paramount for efficient compliance and risk management.
