AI-Powered Financial Data Governance and KYC/AML Standardization

Overview

About the Client

A regional investment company managing 12 billion dollars of assets in high-net-worth and institutional individual portfolios was faced with increasing regulatory compliance issues.

The company operated its own trading platforms, Portfolio Management Systems, numerous custodian bank connections, and old-fashioned reporting tools, all creating client and transaction data in various formats. With increased scrutiny from regulators like the SEC and FINRA, which require audit trails that are comprehensive as well as accurate client reports and monitoring of risk in real-time, the company’s manual data aggregation processes were not able to keep up with the demands of regulators or offer the quality of data required for assurance of compliance.

Challenges Faced

The investment company was plagued by crucial data quality management issues, which led to regulatory risk as well as operational inefficiencies. The transaction data came from six custodian banks as well as trading platforms with inconsistent formats, using security identifiers such as CUSIP, ISIN, ticker symbols, and internal codes being used in a variety of ways. The client account data had duplicate entries, outdated addresses, and inconsistencies in beneficial ownership records. This makes Know Your Customer (KYC) verification for compliance very difficult.

The compliance team put in more than 200 hours each month manually reconciling position data across different systems to produce the required reports for regulatory compliance, and had an error rate of 12% that required significant corrections and submissions. Performance reporting for the portfolio was impacted by delays in data, with month-end reports usually delivered between 8 and 10 days after the close of the period because of manual processing and validation of the data.

The firm was under more scrutiny from regulators following two audit findings relating to audit trails for transactions that were not complete, the delayed reporting of suspicious activity, and a need for a complete system for ensuring the integrity of data.

Our Solution

Blueflame Labs deployed enterprise-grade AI data processing services that are specifically designed to meet the regulatory requirements for financial services. Our data cleaning engine analyzed all transaction feeds with financial industry-specific AI models that recognized information masters for security, corporate action, and the regulatory reporting requirements.

The solution was able to standardize the security identifiers in an underlying reference database. It also automatically reconciles position data between custodians as well as internal systems, validates data from clients against the requirements of regulatory agencies, including CIP as well as KYC standards, and flags anomalies like unusual transaction patterns or missing data elements. Our data mapping layer intelligently made a single golden record for every client account, combining data from CRM platforms, account opening systems, as well as custodian records and valuable ownership databases.

This AI automated data pipeline allowed for constant data quality monitoring, with real-time validation against business rules, the automated creation of an audit trail to track all transformations of data, and complete data lineage tracking for readiness to undergo regulatory inspections. Real-time data analytics offered compliance dashboards to monitor the most important indicators of risk, as well as automated reports for alert systems to detect potential compliance violations that require immediate attention.

Results

The firm’s investment team made dramatic improvements in regulatory compliance as well as operational efficiency:

• 99.8 percent accuracy of data for regulatory reporting, removing the need for resubmissions
• 200 hours of compliance staff time were saved each month, and moved to more valuable risk analysis
• 87 percent reduction in reporting cycle time from 8-10 days to same-day delivery
• 100 percent audit trail coverage and full data lineage to support regulatory exams
• Monitoring of compliance in real-time replaces manual quarterly reviews
• Zero audits of regulatory compliance findings on data quality during subsequent audits
• Improved client confidence by speeding and making portfolio reports more accurate