Data is the driving force behind every decision, business process, and risk and compliance effort in financial services.  Bad data quality poses all sorts of risks, from misguided financial decision-making or misreporting to regulatory investigations and public image damage.

BFSI (banking, financial services, and insurance) companies must ensure that their data quality controls are working, clear, and consistent with business and regulatory needs in a more challenging regulatory environment where global standards are constantly evolving.

Below, we consider the drivers behind BFSI data quality challenges and needs and its role in facilitating stronger risk management and compliance practices.

Defining Data Quality for BFSI

Data quality is a broad umbrella term for any quality of data: it encompasses all data properties, including accuracy, completeness, consistency, timeliness, validity, and reliability. Because BFSI is a field where data not only determines the course of trade but also strategic business decisions, data quality must be monitored and validated across all these segments.

For example, having the correct customer information for both Know Your Customer (KYC) and correct transaction information for Anti-Money Laundering (AML) reviews. Data that’s incomplete or outdated biases risk calculations, and inconsistent data sources can wreak havoc on financial statements. For these reasons and many more, BFSI companies require a high-level data quality infrastructure.

What Does Data Quality Mean for Risk Management?

BFSI is a very high-risk sector where bad data can make financial institutions unable to quantify and mitigate risk, exposing them to:

• Credit Risk: Incomplete credit or finance records result in bad credit reports that lead to more NPLs (Non-Performing Loans).
• Operational Risk: The bad data may lead to errors that impact customer satisfaction and business performance. Data issues also generate false predictions, resource issues, and outages in basic banking operations.
• Market Failure: Incorrect data can cause erroneous market risk estimations, which will defraud the organization of funds.
• Data Governance Risk: Bad data quality leads to non-compliance with mandatory reporting, KYC, or AML, which can result in large fines and reputational damage.

Regulatory Compliance and Data Quality

Regulators across the globe are setting strict limits on how BFSI data is stored. Mandates such as the Basel III Accord, the General Data Protection Regulation (GDPR), and the FinCEN requirements require BFSI organizations to provide transparency, accuracy, and accountability with regard to data quality.

Basel III agreement: Implores strong data quality control to secure sufficient capital reserves and bank-based credible risk calculations.

GDPR: Although GDPR is mostly a data privacy law, it also refers to data accuracy, particularly in the case of personal information. GDPR data-error fines and reputational costs could reach billions of euros.

AML FinCEN: Financial institutions must report suspicious transactions pursuant to anti-money laundering laws. AML algorithms are successful and precise only with reliable data.

Bad data can mean penalties, suspension, or reorganization. Regulatory violations can have more than just financial consequences: business disruption, brand erosion, and damaged customer confidence can also be at stake.

International Data Quality Standards and Industry Standard Specifications

In compliance with regulatory and risk management policies, BFSI providers have to adhere to global data quality standards. ISO 8000 standard, for instance, is the industry standard in BFSI, that defines the requirements for data quality across critical attributes. This, along with compliance with FINRA and DMBOK standards, is what BFSI embraces more and more in terms of data governance. Through these metrics, BFSIs can harmonize data quality activities with global standards to be more efficient in their operation and competitive in their position.

While those challenges and risks are very real, having a strong approach to data quality can bring BFSI many benefits.

BFSI and Digital Transformation

The BFSI industry is experiencing digital transformation through advanced analytics, AI, machine learning, and big data technology. Digital transformation might challenge data quality as the volume and variety of data increases. At the same time, it is also an opportunity for data quality improvements by leveraging automated data checks, real-time monitoring and anomaly detection.

Some banks, for example, use machine learning models to recognize suspicious transactions and spot fraud. AI tools can also automatically correct errors in data that will keep data integrity from decreasing as the data quantity increases. As BFSI organizations transition to digitalized operations, data quality will play a key role in the success of digital transformation and ensuring that technology investments are secure against data breaches.

Data Quality Leads to Better Business Processes and Reduced Operating Costs

High quality data can save significant amounts of money in data storage, error correction and regulatory reporting. Data reconciliation, validation, cleansing, and data cleansing is often the cost incurred by BFSI companies because of poor data quality. Through a proactive data quality approach, they can be automated, redundant processes eliminated, and operating costs mitigated.

Data governance practices such as data validation, real-time quality control, and identifying data issues ahead of costly errors will sustain this. Data quality can also enhance cross-functional operations such as compliance, risk, and customer service. Once the data is safe, workers are more intelligent — they focus on contributing value, not rectifying data.

Enhanced Customer Trust and Experience

The BFSI market success is based on customer belief and faith. Quality data is what drives personalized, accurate and timely services. Poor quality data causes service interruptions, transaction errors, and customer complaints. With proper data quality, BFSI companies can offer personalized financial products, enhanced communication and optimized experiences. Also, data quality supports customer data safety and ethical usage per data privacy regulations such as GDPR.

Establishing Data Quality Management Systems for BFSI

There are a few key things to consider when designing a comprehensive data quality system, including:

• Data Governance: Data stewards, data custodians, and compliance officers handle the data standards across the organization, and they rely on a data governance framework with policies, roles, and responsibilities.
• Data quality monitoring solutions: BFSI companies must adopt data quality testing software to identify false positives, anomalies, and real-time failures for proactive maintenance.
• Data Lineage and Traceability: Data lineage and traceability allow data source, transformation, and use to be accountable and transparent to regulatory authorities.
• Audits & Monitoring: A data quality system must be continuously monitored and audited on a regular basis to identify any emerging data quality issues and enable BFSI institutions to quickly act.
• Employee Training & Awareness: Employees should be trained and informed on the importance of data quality and how to keep updated with the data standards to implement a successful data quality program.

Final Thought

BFSI data quality is a strategic imperative for risk, regulatory compliance, customer confidence, and efficiencies. When it comes to a digital economy in which data is both a gift and a curse, data must be high quality for BFSIs to flourish.

Considering evolving regulations, data quality will always remain at the core of BFSI resilience and competitive advantage. BFSI organizations that invest in data quality will be able to join the world’s standards, stay on-side, and scale.