Big Data solutions in banking and financial services

It’s not a secret that Big Data solutions have a long history in banking and financial services. Big Data comprises of large volumes of unstructured data, which require organization and categorization for further prognosis and business strategies. Storage and management is another characteristic of Big Data, where information comes in huge bulks and in need of high-speed processing. The varying forms of incoming data also complicate the processing, as it can be presented not only in a form of structured information about the client, but also as photos, audio, or video files. Banks, even if compared to other organizations, possess huge amounts of data (including client information). Constant innovations there have become a necessity for successful functionality and improvement of service. So what are the Big Data solutions for banking and finance?

1. Management of client data

As it was mentioned above, banks possess huge bulks of data on their client base. By using Big Data, artificial intelligence (AI) has learned to predict clients’ needs: answers to probable questions concerning service, promotions, helpful suggestions, etc. Solutions based on Big Data can predict the clients’ needs and provide the way-outs even at the slightest hint of difficulties. AI, responsible for processing the data from the client’s profile, assesses their current behavioral patterns, development stage of their business, and other parameters. Furthermore, Artificial intelligence concludes about the potential questions the client may ask, offering its assistance.

“Artificial intelligence processes huge loads of data to give the necessary recommendations. The client will have the ability to see them by simply entering their personal cabinet in online banking or via a mobile app. Such individual advice is compilated by sorting through hundreds of possible topics and predicted according to the latest client activity.”


Still, innovations don’t end there. The client’s data keeps on working for the client even beyond the support service. Some banks have already started implementing a system, aimed at suggesting specifically chosen services directly to the clients, based on their purchase history and transactions. The services offered are individually selected by the AI, thus helping to avoid “spam” and a stream of unnecessary offers and promotions.

2. Assistance with your client’s business

Client data collection can be of service for the client’s business as well. Correlation between the number of bank transactions and the placement of outlets will allow banks to give recommendations to companies about placing new subsidiaries and offices. Banks themselves can benefit from the solution by analyzing the workload of ATMs and their own affiliates and identifying where to place them in bigger numbers.

“Some banks, for instance, compile reports and collect analytics which are later sent to the business clients in a way of a subscription service. Such analytics helps executives to make informed management decisions and keep track of the latest trends and tendencies for the subsequent creation of new products and services.”


3. Fraud detection and prevention

Big Data allows not only to help the client’s business succeed but also to prevent fraud. If a person had a long history of being a bank’s client and used to follow the same patterns, sudden atypical actions from their side can prompt the bank about potential illegal activity and conduction of fraudulent schemes. Big Data there is processed by the means of machine learning, NLP technologies, and data collection from external sources.

4. Feedback management for improved customer service

Clients’ data collection is carried out both from transaction information in banks and review left on social media (and their similar) activity on the Internet. Many banks have long put into practice the analysis of client reactions to their products and services for further improvement and addition of the new ones. Information, collected from the clients, allows banks to timely eliminate the problems and control their own brand’s reputation.

But this is not the end - besides the solutions mentioned above, Big Data facilitates risk-management, assesses the creditworthiness of clients, checks the reliability of borrowers (scoring), etc.

Yet, Big Data solutions don’t realize their full potential without some additional support. As it was mentioned before, automatization is used for a successful structurization of information and prompt data collection, machine learning, and artificial intelligence for the recommendation services.

Invento Labs has vast experience of working with Big Data. More information about our solutions you can see in our cases.

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