In today’s digital age, data has become the new currency for businesses across industries. For banking and financial services firms, however, data represents far more than just a valuable asset – it’s the lifeblood that powers critical operations, drives strategic decisions, and fosters innovation. Yet, despite the technological advancements in recent years, many institutions struggle to fully harness the potential of their data assets.
The challenges they face are complex and diverse. Disruptive forces like higher interest rates, tighter money supply, and tougher regulations are creating an uneven economic outlook. Meanwhile, technological shifts like generative AI and open data are reshaping the industry’s foundations. Customers expect seamless digital experiences and personalised service as standard, not nice-to-have.
In this intricate landscape, maximising data’s potential through strategic management, robust governance, modern infrastructures and data-savvy talent is vital for driving innovation, managing risks and building lasting customer relationships.
Here are seven practical steps to maximising the potential of data in banking and financial services.
- Tame the data ecosystem complexity
Financial institutions like global banks grapple with data scattered across retail banking, commercial lending, investment banking, and wealth management divisions using disparate systems. To gain a unified view, they must integrate this data into a centralised Master Data Management (MDM) platform. For example, a bank could consolidate customer data across divisions to provide a 360-degree view for tailoring products, services, and financial advice.
Achieving this requires a thorough assessment of existing data sources, systems, and processes. Firms should map their data landscape, identifying critical data entities, owners, and interdependencies. This lays the foundation for consolidating and harmonising data into a single, trusted repository accessible to all relevant stakeholders.
- Prioritise data quality and availability
Data quality is critical for areas like risk modelling, regulatory reporting, and credit decisioning at banks. Establishing metrics like trade data accuracy rates and loan application completeness scores is crucial, as is digitising paper-based processes. An investment bank could validate data integrity for its trading book through automation to meet Basel III requirements.
Automated data profiling and cleansing tools can identify and resolve quality issues – essential for the effective use of AI-driven tools – while data enrichment techniques enhance datasets with additional context like appending geographic data. Regular monitoring and continuous improvement processes safeguard data quality over time.
- Ensure regulatory compliance
Banks and insurers face stringent rules like GDPR, Basel III, Solvency II, and anti-money laundering (AML) regulations. This makes robust data management with governance frameworks, data lineage, and access controls vital.
Firms should conduct thorough assessments to understand their regulatory obligations and map data requirements accordingly. Data governance policies, processes, and roles must be clearly defined, with mechanisms for enforcing standards, managing risks, and maintaining audit trails.
- Modernise the data infrastructure
Legacy infrastructures at large financial institutions can hinder innovation – especially in the case of mergers and acquisitions. Migrating to cloud data platforms enables real-time data integration across banking products/services and advanced analytics capabilities. An exciting example use case could be a global bank which moves to a modern data fabric to fuel an AI-powered, cloud-based loan origination system.
This may involve implementing API-driven architectures or adopting microservices for greater flexibility. Scalable, future-proof infrastructures enable real-time data processing, advanced analytics like machine learning, and seamless integration with emerging technologies – positioning firms for long-term success.
- Establish robust governance frameworks
With comprehensive MDM and data governance, firms ensure accuracy and security of critical assets like customer, product, and risk data. A defined operating model with data owners, stewards, and council is key.
A well-defined data governance operating model should establish clear roles, responsibilities, and decision-making processes. Data stewards and owners should be appointed to oversee specific data domains, while a data governance council provides overall guidance and oversight. What’s more, policies, standards, and processes should be documented and communicated across the organisation.
- Build a compelling business case
Quantifying benefits like streamlining regulatory reporting costs, increasing cross-selling revenue, and mitigating credit risk builds a strong case. A retail bank, for instance, could showcase ROI from an MDM solution providing a single customer view for targeted marketing and reduced IT costs from system consolidation.
Building a strong business case also involves highlighting success stories and use cases from industry peers to reinforce the value proposition. Clearly demonstrating the competitive advantages of a robust data management solution is crucial for securing stakeholder buy-in and necessary resources.
- Cultivate data-driven talent
As data becomes crucial for areas like credit modelling and fraud detection, hiring data scientists, AI experts, and analysts is paramount. These professionals can drive data-driven initiatives, extract valuable insights, and foster a culture of data-driven decision-making.
Firms should invest in training and development programmes to upskill existing employees and build data literacy across the organisation. Collaborative workspaces, access to self-service analytics tools, and incentives for data-driven innovation can help cultivate a data-centric culture.
Data drives the future of financial services
Implementing these steps is no small feat, but the rewards are substantial. By maximising their data potential, financial firms can gain a comprehensive understanding of customers, markets, and risks – enabling personalised experiences, informed decision-making, and proactive risk mitigation.
Moreover, data-driven innovation can fuel the development of new products, services, and business models, opening up new revenue streams and competitive advantages. For instance, AI-powered chatbots and robo-advisors can enhance customer engagement, while predictive analytics can optimise trading strategies and portfolio management.
As the financial landscape continues to evolve, data will remain a critical differentiator. Institutions that prioritise data management and leverage their data assets strategically will be better equipped to navigate challenges, seize opportunities, and thrive in an increasingly data-driven world.
By Andy Baillie, VP, UK and Ireland at Master Data Management (MDM) specialists, Semarchy