The financial services industry relies on data about individual transactions, customer insights, credit scores and more to understand their customers and the markets they operate in. Data helps detect fraud and stay compliant with privacy and financial regulations, and many financial services companies have invested heavily in data initiatives to organize and improve their use of data.

Yet, most firms struggle with poor data quality, silos and governance issues that limit AI and analytics value. This article covers the root causes of these nine challenges and offers solutions that help companies build reliable, trustworthy data that’s ready for today’s AI and predictive analytics needs.

What Is the Importance of Quality Data in Financial Services?

Quality data affects all departments of a financial services company, which is why fragmented, unreliable, or ungoverned data can quickly cause confusion and slow decision-making. Quality data should be trustworthy in that it’s the same across the organization and can be relied upon for analytics and decision-making. Trustworthy data allows financial services companies to

• Better understand their customers
• Communicate effectively across departments
• Detect and defend against fraud
• Build reliable models and forecasts
• Stay compliant with regulations
• Innovate effectively based on consistent data analysis

Financial services companies run on data, and making that data governed, consistent and reliable leads to growth and innovation.

9 Data Problems in Finance (with Solutions)

These are the nine most common data quality problems that financial services companies encounter when they begin really looking at their data to put it to use. Any of these challenges on their own could severely impact the company’s ability to serve their customers, and unfortunately, many of these challenges can cluster. But we’ve offered some key solutions to help you overcome these challenges.

Data Challenge #1: Fragmented Customer and Account Data Across Systems

Data that originates and stays siloed in disconnected departmental software like CRMs, loan origination systems and trading platforms prevents a customer 360 view, as several conflicting or incomplete customer profiles exist across the company. This fragmentation means that personalization efforts fall short because they can’t reconcile transactional data, records aren’t compliant with regulations like Know Your Customer (KYC) and Anti-Money Laundering (AML) and reporting can be delayed or inaccurate.

Solution: Centralized customer and account data within an MDM that uses matching, survivorship and hierarchy management to create a single source of truth. This centralized data can be used for reliable compliance, agile management reporting and customer personalizations. All of these needs happen at the speed of the business. A master data management solution for financial services like Profisee that integrates with core financial systems and departmental tools like CRM can deliver a complete 360-degree customer view and streamline KYC and AML compliance.

Data Challenge #2: Unreliable and Outdated Financial Data Sources

Poor data quality — unreliable data that is wrong, incomplete or changes between systems — means the company misses opportunities that reliable data surfaces. Outdated customer information or mismatched customer identifiers can delay or fail to surface householding opportunities and share of wallet opportunities for upsell and cross-selling. According to PWC, banks can generate a 70% return on existing customers, which is a significant revenue boost without the need to find new customers.

Further, the impact of bad data means the company isn’t able to accurately forecast and analyze current reporting, not to mention regulatory reporting. Even if the company implements a data quality solution and data initiatives, without ongoing maintenance, master data quickly deteriorates.

Solution: Let’s consider a real-life example from the financial services company Greystone Financial. Greystone grew share of wallet with MDM by implementing technology that produces and maintains reliable data through continuous data quality monitoring, automated data validation and built-in stewardship workflows. Greystone’s story shows how data management addresses data quality issues by building the foundation that supports data quality efforts.

By matching internal data with external sources, and validating both, Greystone Financial was able to improve their loan origination and servicing. Automated data profiling and validation with a tool like Profisee allows data professionals to work by exception and seamlessly maintain high-quality records across multiple domains.

Data Challenge #3: Rigid Data Governance Frameworks

Strict governance models can be too rigid for many business users, creating delays and bottlenecks while business users try to match data to the requirements. Often, these overbearing governance frameworks cause teams to create their own unregulated shadow datasets where they can work faster by bypassing governance. Shadow data reduces the overall reliability of the organization’s data.

Solution: Ensure data quality with governance that takes a collaborative approach in consultation with business teams that integrates real-world feedback and balances that with regulatory or governance needs. Then give business users self-service access while maintaining centralized oversight of governance in action. Using an MDM with federated governance models that outline clear roles, policies and workflows can greatly simplify this process, as the documentation becomes practice and can adapt to changing business needs.

Data Challenge #4: Complex Privacy and Compliance Requirements

Financial services companies must navigate a complicated regulatory landscape that requires audit trails for key regulations including GDPR, CCPA, BCBS 239, SOX and PCI DSS, each of which brings its own set of needs. Without reference data and lineage tracking to identify historical account and data changes, the company is ill-prepared to begin the audit process.

Solution: Centralize reference data management and lineage tracking across the organization using MDM that automates lineage tracking by logging each change for master data domains. With automated MDM, the team can access compliance-ready audit logs quickly to ensure consistent regulatory and risk management. AXIS Capital standardized its reference data, allowing it to become audit-ready and create new customer offerings from their insights.

Data Challenge #5: Overwhelming Data Volumes and Reporting Demands

Financial services companies quickly create and then just as quickly get overwhelmed with all the data used to run their business, not to mention the added weight of real-time transaction data and continuous reporting demands. Data production like this quickly outpaces manual reconciliations and spreadsheet reporting, which are too slow and don’t provide the necessary context.

Solution: MDM automation and scalable MDM frameworks can work together to handle expanding data volumes without manual reconciliation. Automated data is more reliable across the organization and enables faster reporting. Automated MDM from Profisee helped AXIS Capital to scale across domains and integrate with analytics tools, streamlining their reporting workflows and bringing more reliable insights.

Data Challenge #6: Dependence on IT for Data Access and Insight

IT reporting and analysis dependence is frustrating for all involved: Business units lose time sending requests and waiting for reports, and IT teams lose time gathering datasets and reports that could be self-service. All this wasted time and back-and-forth slows down decision-making for business units and limits their innovation capabilities because they only receive the context that they explicitly requested or IT deemed necessary. When IT manages all data reporting, everyone loses.

Solution: Enable data democratization across the organization. By giving self-service access to trusted, governed data for data stewards (authorized users), the organization can move faster, and more stakeholders can work on driving innovation. Use an enterprise data quality tool like an MDM with role-based access controls that quickly provide access to data stewards to empower business users while maintaining governance. Add in intuitive workflows to make maintaining, reporting and analyzing data accessible to all departments.

Data Challenge #7: Complex Data Integration During Mergers and Acquisitions

M&A can quickly grow the company’s footprint and revenue while creating internal chaos. Incompatible software systems and data formats, redundant records across locations and systems and conflicting hierarchies can all impact revenue numbers. Add in confusing or duplicate audit logs, and compliance is also at risk. What was once a promising opportunity quickly devolves into a data nightmare.

Solution: Harmonize data models, deduplicate records and establish unified hierarchies with MDM. Once implemented, master data management automates data governance and newly introduced data from M&A integrates quickly into the MDM system. With flexible software integrations and automated workflows, Profisee helped Greystone Financial integrate records across 33 internal systems and several third-party data sources, rapidly aligning data to improve efficiency.

Data Challenge #8: Lack of Ownership and Accountability Across Data Domains

Unclear data ownership and confusing accountability can lead to inconsistent standards, neglected stewardship and overall unreliable data, which can undermine any data initiative. When sales and manufacturing can’t decide on who owns product descriptions, no one does, effectively. And when data ownership doesn’t go beyond who has access to the most recent spreadsheets, finance companies can’t rely on their data.

Solution: Tighten control by establishing clear data ownership models that identify key stakeholders for approvals and decision making. Define and assign stewardship roles to clarify accountability, then document and track KPIs to back up that accountability with numbers. Use Profisee’s workflow automation tools to add a hands-off approach to assigning, tracking and organizing unreliable data into good quality data across domains, lightening the load for data stewards and stakeholders.

Data Challenge #9: Unprepared Data for AI, Machine Learning and Predictive Analytics

Despite the potential of AI programs, most financial institutions lack AI-ready data because their data is unstructured, ungoverned and fragmented. Even more importantly, it also lacks context, which AI needs to truly make sense of data, no matter the quality. Data + context = information, and information is what AI needs to unlock real insights and decision making power. Without AI-ready data, you risk unreliable models, compliance risks and a lack of explainability of results.

Solution: Build an AI-ready data foundation of trusted master data, standardized schemas and metadata management. Achieve reliable, structured and governed data inputs that are AI- and advanced-analytics-ready with automated governance and trusted workflows. MDM software designed to make sense of your data like Profisee is a critical enabler of responsible AI systems for financial services, protecting data reliability and customer security.

Best Practices to Manage Data Issues in Finance

Data technology is only one part of a holistic approach to improving data reliability in finance. Follow these best practices to build a data foundation that supports advanced analytics and innovation:

• Establish clear data policies and standards. Define how data enters, moves through and exits the organization’s systems. Then back up those policies with actionable goals, KPIs and accountability.
• Implement continuous data monitoring and stewardship. Take data monitoring out of spreadsheets and use automated systems that follow data quality rules to flag inconsistencies and errors. These tools help data stewards work by exception and cover many more data domains.
• Leverage modern MDM and integration tools. Data exists in all corners of the financial services company, even those departments that are siloed. Use robust MDM software with integration tools to reliably bring all data to light, cleanse it and send it back out to the company.
• Prioritize data for analytics and AI. While it may feel right to begin managing your data with smaller projects, it’s a good idea to start by tackling the domains and data that will make the most difference for your analytics or AI projects. Seeing the progress and insights brought on by digital transformation software in these areas will fuel larger and more complicated data projects later.
• Promote cross-functional collaboration. IT can’t wrangle the entire company’s data alone. Tap key stakeholders and business users in all departments to understand how people use data every day and build governance and policies around those practices.

Build a Trusted Data Foundation for Financial Growth with Profisee

Trusted data underpins every strategic initiative in financial services, including digital transformation, advanced analytics, reliable forecasting and AI. MDM not only helps to consolidate, cleanse and build the reliable data foundation your company needs to support business initiatives, but it also helps keep that data reliable and business-ready.

Want to see how Profisee solves the leading data challenges in financial services by unifying data, ensuring compliance and accelerating digital transformation? Book a demo today.