Open Banking is Democratizing Big Data as a Force for Good

by Analytics Insight

February 28, 2022

Open banking

Open banking facilitates the collection of big data, that when thoroughly analyzed, could lead to informational discoveries

Open banking is most often seen as an opportunity to share financial data with third-party service providers to simplify and automate processes, as well as create new opportunities to further services and products. Apart from helping businesses evolve and develop their offerings, open banking also facilitates the collection of big data, that when thoroughly analyzed, could lead to informational discoveries. These findings could then be utilized further to create solutions to tackle negative social and financial trends that could directly help individuals.

Each financial transaction performed by a person generates rich, up-to-date data, and before the emergence and adoption of open banking, this valuable information was limited to a small number of organizations. Open banking, however, is helping democratize this data, allowing businesses and organizations to extract insights and analyze the findings to gain a better understanding of their customers and social trends as a whole. 


How open banking enabled big data is changing fintech

According to research by IBM, approximately 2.5 quintillions (for reference, 2.5 followed by 18 zeros) bytes of data are generated daily, with 90% of this data being generated just over the last couple of years. Big data and its analysis are changing the way financial service providers operate, helping them make quicker decisions and be more informed about their product offerings, operations, and marketing efforts. Big data has enabled finance companies to create new types of services and tools that would not have otherwise been available, such as credit cards with integrated fingerprint scanners and PFM apps that forecast customer behavior and spending habits.


Fintech’s bigger impact beyond customer satisfaction

Open banking allows for the ultimate connections to be formed between banks, third-party financial service providers, and customers, sharing client financial data to personalize services and automate processes. The data gathered, however, can be taken further to analyze customers to help identify and tackle negative social trends, such as gambling addictions and poor financial management. 

By connecting bank accounts directly, customer data, such as transaction history and quantity of similar transactions can be gathered in large amounts and patterns can be highlighted through further analysis. Gathering never-ending amounts of data on customers will provide invaluable insights into how and where customers spend their money allowing for a more personalized approach to helping individuals in debt or with ongoing online addictions. 

In cases of debt due to unhealthy spending habits, tailored plans can be created via personal finance management platforms that take into account typical trends and patterns gathered across numerous clients, as well as data sourced on specific users and their habits. By combining these two sets of data, predictive analytics can create accurate pictures of how clients will act and what plans will be helpful. 

Similarly, big data can help highlight which banking and PFM users can be deemed “at-risk” of falling into debt, based on the number of transactions, overreliance on loans, and frequent overdrafts, comparing them to other users with similar histories. Users that fall into this category can be targeted with personalized finance tips, expert advisory services, and offers for more in-depth budgeting solutions. 

Additionally, data can be gathered and analyzed on a variety of automated subscriptions, taking into account information on whether a paid-for service is being utilized. If it’s unused, AI-powered analytics tools can automatically cancel subscriptions and even contest fees. This helps manage finances for users across the board, as well as automatically alleviating stress from already struggling clients. 


Customer protection through the use of pattern analysis

Users that suffer from online gambling and gaming micro-transaction addictions, or users that exhibit similar spending habits within their transactional histories, can be limited by third-party services or blocked off from them completely if enough red flags are found within their spending data. This is similar to Monzo’s self-exclusion gambling block, where users set an agreement with the bank to cancel their transactions to companies with specific merchant codes that identify them as a gambling business. 

To reverse this block, users need to follow a long process, namely have a detailed conversation with the customer support team on their gambling habits, as well as wait for 48 hours for the option to turn the block off to appear following the chat. This “friction” is set in place to help gambling addicts overcome sudden gambling urges. A similar system can be put in place by the providers and banks themselves using big data to identify addiction warning signs in client behavior. Additionally, lenders can use this information to prevent loans from being granted to high-risk individuals. 

While some banking clients may be nervous about data sharing, especially when it comes to such valuable information as to their finances, gathering large quantities of statistics and research on consumers is actually beneficial to the users themselves. The information helps banks and service providers target specific needs and issues, as well as offer more personalized services. When negative trends are noticed, big data can further help in halting the progression of the issue and offer well-researched solutions. 


About the author

Rolands Mesters is the CEO and co-founder of Nordigen, the only freemium open banking API that connects to more than 2,100 banks, making it the largest network of bank connections in Europe. Rolands is a sales and growth hacker who is passionate about fintech and alternative lending. Nordigen started out as a data analytics company that builds solutions for categorizing and analyzing bank account data. In December 2020, the company launched Europe’s first free open banking account data API. Rolands has been featured in the Forbes Latvia 30 Under 30 list as well as being featured in TechCrunch, Sifted, and the Financial Times. Rolands regularly shares fintech insights and analysis on open banking at top international fintech events and is considered one of the foremost experts on open banking worldwide.



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