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Making Good on the Promise of Open Banking Requires Technology and Solid Execution

Open banking is here and is showing no signs of slowing down. According to a recent Finastra survey, 56 percent of US financial institutions (FIs) surveyed regard open finance as a “must have”, up from 45% in 2021. Globally, the open banking market is expected to grow to $43 billion by 2026 from its value of $7 billion in 2018.

Open banking is the practice of securely sharing financial data between banks and third-party service providers, such as fintech apps. Before open banking became available, consumer financial data was controlled by big banks. With the advent of open banking, consumers can now manage their financial information, accessing it across different platforms, benefiting from a smoother, more personalized experience in the process.

And that’s the key. Consumers rely on open banking to power their digital financial experiences. Regulators are finalizing rules to ensure consumers have the right to share their banking data with whichever service providers they choose. However, energy spent fighting against open banking is a waste. It’s also a missed opportunity for banks.

FIs must change – analytically, operationally, and even culturally (no longer owning customer data) – to benefit from the open banking revolution.

One of the first things to think through is to understand in what ways this data will be used and how it can result in a competitive advantage. Fintechs recognized this early on, fostering the explosive growth of Buy Now Pay Later (BNPL) by embracing alternative and shared open banking data to onboard new customers. By using open banking data, the provider can learn more about the customer and deliver a more personalized experience.

Use of open data must also be operationalized. Today’s technology, such as artificial intelligence and machine learning, automated credit risk decisioning, data marketplaces (especially for underbanked and new-to-credit consumers), and data integration can all be of benefit.

Increasing Efficiency: Machine learning algorithms can enhance credit risk models, processing vast amounts of data quickly and reducing the time and person-power needed for risk assessments and credit decisioning. This also can reduce the time-to-market on new products.

Reducing Costs: Automating the credit risk decisioning process reduces the manual labor required, allowing FIs to allocate resources to other strategic initiatives that can help grow revenue, and improve the customer experience.

Enhancing the Customer Experience: Focus on frictionless onboarding and customer management, with faster credit decisions, digitized processes, and more hyper-personalized product offerings (including everything from interest rates to loan terms, upsell/cross-sell offers, and even optimized collections strategies).

Improving Risk Management: Advanced analytics can enable FIs to identify key patterns and trends in customer behavior, ensuring more accurate risk assessments and reduced losses due to defaults and improved fraud detection.

Enabling Agility: With more flexible, user-friendly decisioning technology that can ingest in real-time open banking data, FIs can make changes to decisioning workflows quickly, respond to market shifts, meet changing consumer demands, and launch new products faster to stay ahead of the competition.

Fostering Innovation: Executing on all the above points (with more automated decisioning, advanced analytics, superior data integration, improved efficiency, etc.) means FIs can foster a true culture of innovation.

Every decision point across the customer lifecycle – from credit risk evaluation to cross-sell to collections – stands to benefit from the real-time, contextual insights that open banking data can deliver.

Open banking has the potential to revolutionize how FIs make decisions about their customers while improving efficiency, reducing costs, and sharpening their focus on delivery of customer-centric products and services.

 

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This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.

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