Unlocking the power of data products in financial services

The financial services sector thrives on data. From managing customer relationships to mitigating risks and ensuring compliance, data drives nearly every key operation. Yet the industry faces a mounting challenge of efficiently leveraging the growing volumes of data being collected. Imagine moving away from traditional, siloed repositories towards personalized, actionable insights delivered via data products. That’s the future for proactive, agile financial institutions.

What are data products?

A data product is a processed and packaged output of data designed to meet specific business needs. These could be dashboards for portfolio management, AI-driven customer sentiment models or platforms for risk assessment. Instead of treating data as a static storage system, thinking of it as an asset that can be actively shaped into tangible business-driven solutions is a hallmark of leading financial institutions.

Why the financial services sector needs data products

Here’s why creating and adopting data products is essential for financial services organizations:

1. Personalized customer experiences:

 Customer data can enable tailored financial advice, targeted marketing strategies, and relationship-based pricing. For example, analyzing spending behaviors can help banks offer dynamic credit card rewards or savings plans suited to individual needs. A global Capital Market an Investment Bank, for instance, implemented a digital platform allowing users to manage portfolios, access market data, and enhance customer engagement.

2. Enhanced risk management:

Data products help banks process real-time data for fraud detection, regulatory compliance, and proactive management of credit risks. Tools like AI-powered fraud monitoring models shorten response times and reduce false positives. ING Bank’s adoption of data mesh provided a framework for rapid, accurate risk evaluations for real use cases.

3. Operational efficiency:

 Automating routine processes like customer onboarding or loan approvals with data-driven tools reduces redundancy and accelerates workflows. A Spanish digital bank’s PSD2-compliant payment gateway ensured seamless SEPA and instant payments, improving operational performance while adhering to regulations.

4. Data-driven decision making:

 By analyzing market trends and customer behavior, banks can develop strategies for product innovation, revenue growth and adapting to fluctuating market conditions. For example, Thoughtworks worked with a Latin American bank to integrate data across functions, resulting in more successful cross-selling in their ecosystem.

5. Regulatory compliance:

Meeting the stringent requirements set by global regulatory bodies is a common challenge in Financial Services. Data products underpinned by robust governance frameworks ensure that data used for regulatory reporting is accurate, up-to-date and secure, reducing risks of penalties.

Financial services-specific use cases of data products

• Wealth management dashboards:

   

  Tools like Saxo Bank's "Data Workbench" give customers and internal teams real-time access to data-powered insights. This not only facilitates better portfolio management but also supports data democratization across teams, empowering decision-making at every level.

• AI-infused payment systems:

   

 Autonomous payment systems, powered by data products, handle transactions at unprecedented speed and scale. SurePay’s advanced payment gateway, for instance, ensured seamless operations for mobile, SEPA, and instant payments during a critical bank launch in Spain.

• Cross-selling platforms:

   

 Data-driven recommendation systems identify opportunities to cross-sell financial products based on a customer's transaction history and life stage.

• Risk prediction models:

   

 AI-curated data products enable advanced credit evaluations, anticipating financial risks with better precision and minimizing exposure.

What about data mesh?

The financial services sector increasingly leverages data mesh, a decentralized approach to data governance. Traditionally, banks have centralized data under IT, creating bottlenecks and limiting its usability. With data mesh, businesses distribute data ownership within operational domains, enabling faster time-to-market solutions.

For example, ING Bank implemented a data mesh proof of concept using Google Cloud, allowing diverse teams to collaborate and operationalize financial products swiftly. However, even without fully adopting a data mesh, financial institutions can apply “data as a product” principles to enhance governance and usability.

Principles of a good data product for financial services

To ensure data products deliver maximum value, financial services organizations should follow these principles:

1. Customer-first approach:

 Align with the business and end users early and often, that way every product is fit for purpose with an intuitive UX and offers features and capabilities that address business challenges,and meet the needs of customers. 

2. Discoverable and reusable:

 Even when catering to niche needs, data products should remain accessible to other teams or projects, enabling scalability.

3. Scalable and intuitive:

Products should offer seamless integration and usability for both technical experts and non-technical stakeholders. UX design and the last mile of the product's journey is most  imperative.  

4. Secure and compliant:

  Financial services sectors face strict data protection requirements. Products must integrate robust governance frameworks to meet internal and external compliance standards. Thoughtworks’ use of DATSIS (discoverable, addressable, trustworthy, self-describing, interoperable and secure) principles ensures financial institutions maintain consistent quality.

How to get started with data products in financial services

There’s no need to initiate a complete overhaul to tap into the power of data products. Follow these steps:

1. Identify a use case:

Start small. Work with business teams to identify challenges where data can make the most impact (e.g., stress testing, settlements and reconciliation, cutting loan approval times or improving fraud detection).

2. Build cross-functional teams:

Bring together data stewards, data product owners & business leaders, data analysts, data scientists, UX/UI QA & Testing and IT teams to ensure seamless collaboration. Foster Hackathons and co-creation workshops to build inclusiveness in vision and direction is key driving business value. 

3. Adopt the thin slice approach:

Develop a simple, functional data product prototype that spans end-to-end needs. Gather metrics and feedback to iterate quickly.

4. Focus on Training:

Upskill employees and develop a data-literate culture with stewards and data product managers with a co-creation mindset to drive adoption.

5. Measure Success:

  Track KPIs aligned to business outcomes, such as reduced processing times or higher customer satisfaction.

Why data products are key for financial services transformation

Data products have become indispensable for financial organizations aiming to thrive in a digitally disrupted landscape. By allowing banks and insurers to unlock value from data, they enable more personalized customer experiences, manage risks more effectively, and support smarter decision-making.

For example, a global Capital Market an Investment Bank's platform improved portfolio engagement, while Saxo Bank’s solution reduced cloud costs. These success stories demonstrate how data products can help Financial Services organizations stay ahead in an increasingly competitive world.

The bottom line? When financial institutions treat data as a living, breathing asset, the possibilities are endless.