Data has become one of the world’s most precious resources, and companies can no longer afford not to have a data-first strategy if they want to remain competitive in an increasingly digital world.

This includes the banking and financial services sector, which is facing mounting pressure to leverage artificial intelligence, data and analytics to fend off increasing competition from agile fintechs and digital banks to offer customers more personalized services and instant credit decisions, protect against fraud, streamline their operations, and reduce costs.

However, financial institutions are also facing complex challenges due to legacy IT systems, changing customer behaviors, regulatory requirements, sophisticated financial scams, and data storage and quality. “With the emergence of new data streams and technologies, such as cloud computing, artificial intelligence, and machine learning, the penalties for not staying ahead of the curve can be enormous,” PwC notes in a recent research report.

Here, we explore the importance of data for financial institutions, how it is transforming the industry, and the challenges they face in leveraging what has been described as the “new oil” of the 21st century.

The rise of a data-first strategy

In recent years, the banking and financial services industry has significantly increased its use of data, analytics, and AI technology, such as machine learning and generative AI, to streamline processes, reduce costs associated with manual procedures, and boost employee efficiency. This includes using increasingly sophisticated AI-powered chatbots that can give customers faster responses to their queries.

Financial institutions such as Morgan Stanley and Wells Fargo use generative AI to produce and analyze content and create synthetic data. JPMorgan Chase is also using sophisticated AI-driven virtual assistants to help clients move money worldwide. While many financial institutions are starting to use generative AI, the majority of financial services firms are already using machine learning algorithms to predict, for example, mortgage payment defaults.

Automated trading strategies and personalized investment decisions are made possible through advanced AI models created by banks, including Goldman Sachs and Morgan Stanley. AI is being implemented to detect financial fraud and automate manual tasks such as processing insurance claims or credit card applications.

Banks have also started leveraging customer data to offer tailored digital solutions and introduce innovative revenue streams with new services and products, such as robo-advisors, which are algorithm-based platforms designed to provide financial and investment management advice and even make lending decisions, according to PwC.

What are the challenges?

Although AI and data collection have the potential to enhance banking services and increase security, there are still significant challenges and risks to keep in mind. These include training staff on how the machines are processing data to ensure that any regulatory breaches can be identified. Employees will also have to develop a critical eye when it comes to data and only rely on it with proper checks and balances in place.

“It’s critical that these AI-driven services are transparent and without bias. As the technology still requires human interactions to train the algorithm and input quality data, there is large room for unintentional bias to be coded into these services,” PwC warns.

AI technology requires human interactions to train the algorithm & input quality data. © Getty Images

Another challenge is that AI models must be properly governed and maintained to not produce bad-quality outputs. The quality of data also must be high enough to make the outputs from the models trustworthy.

It is also important to note that European Union GDPR regulations largely govern privacy management risks in the bloc’s financial sector. This means that the industry requires customer consent for their data to be used and allows them to modify their original choice easily.

The future is collaborating with fintechs

According to PwC, open banking, and application programming interfaces have offered third parties and fintechs a valuable entry point into financial services by allowing collaboration and access to incumbent banking data.

This has opened up the potential for significant innovations and collaborations within the industry, such as UK bank Natwest leveraging an open banking data model with a fintech partner to provide customers with detailed and real-time spending insights, it adds.

Established financial institutions outside of the EU are also teaming up with popular social media platforms to expand their reach and provide customers with digital services that they may not have otherwise been able to access, according to PwC. For example, customers can now apply for credit cards and loans through their mobile phones on these social media platforms, which offer unique capabilities not available through traditional banking services.

“Gaining access to a wider set of customer data through these platforms allows banks to match offers and products to credit card holders and offer customized products,” PwC says.

Customers can now apply for loans through their phones on social media platforms. © Getty Images

How can Sopra Banking Software help?

We are in the process of developing the data analytics platform for our next-generation Sopra Banking Platform. As part of our data strategy, we will help our customers comprehensively leverage data. Through our various offerings and expertise in data strategy, we aim to guide and enable our customers in their data strategies. Already, we have created a robust library of data-driven use cases that we share with our customers to help them identify what areas they may want to explore and add to their roadmaps. 

In a fast-paced financial world, your bank needs more than just data—it needs a data-driven powerhouse: our Data Analytics Platform is a cutting-edge solution that not only collects, manipulates, and generates a wealth of data but also revolutionizes how you leverage it for unparalleled success. 

  • Future-Proofed Data Architecture: Adaptability is key. Our data analytics platform boasts a robust, scalable architecture that evolves with your data needs, ensuring long-term relevance and efficiency for your bank’s data infrastructure.
  • Seamless Data Management and Migration: Handle vast volumes of data effortlessly. With integrated tools, managing and migrating data becomes a breeze while upholding data integrity and security.  
  • Unlock Insights by Combining Data Sources: Combine external and SBS system data for exceptional business insights, enabling smarter decisions and superior outcomes. 
  • Embedded Intelligence and AI: Maximize your data’s potential. Our platform leverages powerful AI capabilities, employing machine learning and advanced techniques to streamline processes, optimize operations, and drive automation. 
  • AI Products for Next-Level Performance: Elevate decision-making with our suite of AI tools. Whether it’s automated credit scoring, streamlined customer claims, or churn prevention, we’ve got tailored AI solutions for your needs. For example, we’ve launched an AI assistant powered by generative AI to help users quickly analyze regulatory text, and this can be expanded to other use cases. 
  • Real-Time Data and Reporting: Stay ahead with real-time data. Our platform offers up-to-the-minute insights, empowering you to make informed decisions and swiftly respond to market shifts.
  • Advanced Business Analytics and Systems Optimization: Gain deeper insights into operations, customer behaviors, and financial performance. Optimize banking systems for enhanced efficiency and customer satisfaction.
  • Customer-Centricity through Data: Personalize experiences and enhance satisfaction by leveraging customer data to understand preferences and offer tailored services.
  • Open Banking Integration: Seamlessly integrate open banking data to expand service offerings and deliver a broader range of financial services to your customers.
  • Partner Ecosystem for Data Use Cases: Collaborate with our partners for pre-built data use cases, saving time and resources while accelerating development.
  • Flexible Third-Party Integrations: Our open architecture allows seamless integration of third-party data sources and AI solutions, ensuring the incorporation of cutting-edge technologies into your operations.
  • Expert Data Consultancy Services: Leverage decades of Sopra Steria’s data management and analytics expertise to maximize your data-driven capabilities and adopt best-in-class technologies.

With our comprehensive features and benefits, the SBS Data Analytics Platform empowers your bank to innovate, elevate customer experiences, and stay competitive in the dynamic financial landscape. Whether it’s dashboarding, personalization, decision support, or process automation, our data-driven platform equips you to achieve all your goals confidently. To find out more, book a demo now with one of our experts!

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Dana Lunberry

Head of Data Strategy

Sopra Banking Software