This article was written by Ankur Gupta and originally appeared on the Collibra Data Quality Blog here: https://www.collibra.com/blog/the-importance-of-data-quality-in-financial-services
Financial services are highly regulated and maintain a strong focus on compliance and risk management. Constantly monitoring data and also reporting it to the regulatory authority is their top priority. Considering that major financial organizations handle enormous amounts of data today, they require data accuracy and integrity at all times to minimize risks.
What is data quality in financial services?
Risk management and regulatory compliance both are strongly impacted by the quality of data. If the customer data is incomplete or the forex values are not updated, the results can severely affect the credibility and bottomline.
Moreover, financial services are always time-sensitive where a single data error quickly multiplies in the downstream processes and is not easy to fix in time.
While data quality in financial services indicates if data is fit for use, its dimensions of completeness, timeliness, accuracy, and validity ensure compliance with the regulations. Data integrity, safeguarding the relationships of entities across the organization, is essential for managing risks.
Why is data quality important in finance?
Customer data can drift and lose integrity over time. Change of addresses or phone numbers may not get updated immediately. Added applications and new data sources may not get reconciled correctly.
These data quality issues in financial services directly impact customer experience, interactions, and transactions, resulting in higher costs and lost revenue.
Financial services also use the same data in reporting, analytics, and forecasting. Data quality issues lead to bad decisions and weak strategic planning, leading to higher expenses and lost customers.
Leveraging AI technologies for improving the efficiency and performance of financial services requires high-quality data to train the machine learning models. Once deployed, the models again need high-quality data to deliver trusted insights. Gartner recognizes data quality as one of the main barriers to the adoption of AI in financial services.
Finally, non-compliance due to poor data quality results in regulatory penalties that ultimately damages brand equity.
How can financial services improve data quality?
Improving data quality in financial services calls for a comprehensive program. A data-driven culture, Data Intelligence for a deeper understanding of data, awareness of the most common quality issues, and technology-driven enablement, all contribute to improving data quality.
Compliance is one aspect where data quality and Data Governance interact, and data quality improvement initiatives designed for constant data monitoring can support both.
Data quality implemented on top of a data governance foundation provides a view of quality in the context of how the business uses data, enabling:
- data stewards to identify and prioritize quality issues
- business users to test what-if scenarios
- business analysts and data scientists to perform analysis with more accurate data
Predictive, continuous, self-service data quality uses ML-driven adaptive rules to predict possible violations and initiate remedy processes. The highly scalable solution quickly helps generate reports and audit data for compliance with regulations such as GDPR, CCAR, and BCBS 239.
By constantly monitoring data quality, financial services have better control over their end-to-end data pipelines feeding the operational and analytical processes. The same trusted data can train machine learning models and drive strategic decisions across the organization.
A practical self-service approach democratizes data quality for all data citizens and business users to proactively identify and help resolve the data quality issues.
Powering trusted financial services with predictive data quality
Predictive and continuous data quality streamlines the time-sensitive financial services to deliver trusted results in real time.
1. Constantly monitoring foreign exchange rates
The world engages in foreign exchange (FX) transactions through more than 28,000 currency pairs that constantly fluctuate. Most banks work with a focused list of FX pairs, combining them with other financial data to run analytics.
Monitoring the quality for such a large data set throughout the day is laborious, demanding hundreds of manual rules for duplicate detection, anomaly detection, or correlations.
Predictive data quality can automatically alert incorrect FX rate data without writing a single rule. With the ML-powered auto-learning approach, predictive data quality runs quality tests against each data set individually to deliver the best and consistent controls across all data sets.
The predictive data quality constantly checks:
- Currency pair tracking
- Duplicate detection for currency pairs
- Anomaly detection
- Automatic correlation and relationship analysis
- Histogram and segmentation analysis
- Schema evolution
2. Predictively tracking intraday positions
Financial organizations process large volumes of near-real-time data on intraday positions. Tracking the positions is a challenging task, especially for correct correlation and no duplicates for any company.
When a specific company does not trade or adjust its position in the day, the missing records also must not raise any false alarm.
Predictive data quality offers real-time outlier and duplicate detection, delivering only the highest quality data pipelines feeding the analytical models.
The continuous data quality operates with inbuilt adaptive rules and analytics to track:
- Intraday positioning data profiling
- Correlation analysis
- Duplicate detection
- Outlier detection
- Pattern mining
- Schema evolution
3. Identifying anomalies and hidden patterns in the security reference data
Financial firms of all shapes and sizes ingest reference data from various vendors such as Bloomberg, Thomson Reuters, ICE Data Services, or SIX Financial Information.
Accuracy of this data is critical for data-driven business decisions, and identifying anomaly values earlier in the data ingestion process significantly reduces downstream complexity.
Another factor that affects the quality of data generated by reporting, exchanges, and source systems is hidden patterns. Finding improbable patterns before they get used in data-driven decisions can considerably save the remediation efforts.
In both cases, predictive data quality can identify securities that violate historical patterns. Applying pattern recognition for cross-column, categorical, and conditional relationships with an adaptive algorithm, the continuous data quality reduces false positives, scales coverage, and quickly models a complex series of checks.
4. Managing credit risk in bank lending
Lending is a significant financial activity for banks, which comes with its own risks. Banks need to be vigilant throughout the loan underwriting and approval process, validating data at every stage to minimize the credit risk.
The adaptive data quality components Profiles, Duplicates, Outliers, and Rules can be trained to validate in real time:
- Credit score validation
- SSN validation
- Loan to value assessment
- Interest rate check
- Duplicate loan applications prevention
- Loan amount range check
- Loan completeness validation
5. Accelerating cloud adoption
The 2020 pandemic has swung the scales in favor of a more cloud-heavy strategy for banks and financial institutions. These changes would ultimately benefit consumers, who are increasingly dependent upon digital services versus in-branch interactions.
Financial services deal with large volumes of data and often need to move or copy data between storage systems. Data integrity is at risk during such migrations, necessitating complete data validation to ensure no change in data.
Cloud adoption provides agility and speed to financial services, helping them scale fast and improve efficiency. But poorly governed and bad quality data prove to be a barrier to cloud migration.
The predictive data quality on top of a data governance foundation helps detect data corruption during migration as it can:
- Validate data as it moves from source to target
- Identify missing records, values, and broken relationships
- Leverage inbuilt workflows to proactively resolve the data issues
- Perform data profiling and cataloging on the source systems to understand the quality of data
- Reconcile data between two data objects validating that data is replicated accurately
- Leverage ML-powered rules to automatically identify potential match duplicates in datasets
6. Monitoring frauds and cyber anomalies in real time
Financial services work with sensitive data, and the value of this data makes them vulnerable to cyber threats. Technology-enabled internet and mobile banking expose online financial transactions to possible security breaches.
As a result, financial organizations are increasingly turning to automation for detecting and defending against cyber attacks.
The predictive data quality can continuously load and process diverse security data feeds at scale for detecting network data anomalies. With timely alerts, network and cybersecurity experts can respond quickly to possible threats.
The continuous data quality performs in real time:
- IP address validation
- Detection of unusual network traffic patterns based on locations
- Identification of suspicious packets based on size
- Detection of malicious activities based on source and destination IP addresses
Today, most financial services organizations recognize the importance of data quality and introduce comprehensive initiatives to improve it. With the right tools for continuous data quality, up to 60% of manual efforts can be saved, and regulatory audits can be completed in a record time of just four weeks.