Banks and insurance companies are embracing these new technologies and the results are impressive. According to a Research and Markets report, the global big data market, estimated to be worth more than $150 billion last year, is expected to reach a value of $350 billion by 2030. The McKinsey consultancy also made public another interesting fact for the insurance sector: due to the use of big data, fraudulent claims have been reduced by up to 80%.
By analyzing data, entities can identify needs and preferences and adapt their offer.
Co-founder & CTO of Coinscrap Finance
What is Big Data and how does it apply to the world of finance?
This term refers to the petabytes of structured and unstructured data that can be used to anticipate the behaviors of banking users and financial institutions and that serve to create more efficient strategies. One of the main benefits of big data and predictive analytics is the ability to personalize customer experiences. By analyzing their data, entities can identify needs and preferences and adapt their offer accordingly.
This causes a major increase in customer satisfaction and retention rates. According to a recent Accenture report, banks that use big data analytics to personalize customer experiences can skyrocket revenue in a short time. They also insist that to become or remain a market leader, every bank has to embark on a journey of constant renewal.
Improving the UX and the customer journey is much easier if we use technology
Another of the challenges facing the sector is that digital channels continue to be perceived as functional, but emotionally empty and aimed at the masses. In short, they are impersonal. Users miss someone showing real interest in improving their financial situation. Banks need to give advice that leaves a mark in order to be able to sell products that involve greater customer implication.
Banks must take inspiration from other industries to rethink this relationship. The consultancy mentions Shiseido, a leading skincare company that uses user information to deliver optimized content. Data from your history, such as your skin type assessment –done online or in a store– is added to a database that feeds an artificial intelligence engine capable of generating personalized insights.
This information allows the company to send personal care packs to each customer with selected products, not only to adapt to the person’s skin tone, but also to the occasion in which it will be used. This new approach can help banks source, organize, and enrich customer data in radically different ways.
Predictive analysis thanks to the “digital memory” of the user, how to use it?
If you already have some gray hair, surely you remember the typical bank worker who automatically remembered your customer profile as soon as you walked in. Well, now we can multiply that knowledge exponentially thanks to predictive analytics. Basically, we need to create a digital repository that contains the history of each individual and… let the magic begin!
It all starts with a collection of data: demographic, transactional, credit, etc. from internal and external sources. We then move on to cleaning and categorizing that data to remove errors, duplicates, and incomplete information. It is time to select the relevant variables and characteristics to be used in the predictive model.
Once these steps are completed, the predictive analysis algorithms are developed: linear regression, decision trees, neural networks, etc. For its evaluation we use techniques such as cross-validation or the confusion matrix. If your accuracy and performance exceed quality standards, it’s time to implement them. All that remains is to periodically monitor the results to maintain their accuracy and relevance.
The current and future needs of customers in the focus of your digital strategy
For banks to become more relevant and effective, they need to move from simply knowing basic demographic and financial information about their customers to understanding the daily life, aspirations and intentions behind every financial product purchase. It is the only way to achieve a lasting bond and anticipate future needs.
Predictive analytics allows customers to have a simpler and more intuitive experience. Making it easy to connect with human advisors or advanced chatbots is a priority for many banks. It has been shown that customers still value – and need – interaction in physical places, which is why it is necessary to make a change in format.
It would be interesting to transform them into advice centers, self-service kiosks or personalized information points. More importantly, branches must take advantage of technology to offer all customers the personal touch and the kind of conversations that neighborhood offices have historically provided.
It is essential that these new experiences are not designed in isolation, but as part of a much broader and more general experience on the part of financial institutions.
About the Autor
Óscar Barba is co-founder and CTO of Coinscrap Finance. He is an expert Scrum Manager with more than 6 years of experience in the collection and semantic analysis of data in the financial sector, classification of bank transactions, deep learning applied to stock market sentiment analysis systems and the measurement of the carbon footprint associated with transactional data.
With extensive experience in the banking and insurance sector, Óscar is finishing his PhD in Information Technology right now. He is an Engineer and Master in Computer Engineering from the University of Vigo and Master in Electronic Commerce from the University of Salamanca. In addition, Scrum Manager and Project Management Certificate from the CNTG, SOA Architecture and Web Services Certificate from the University of Salamanca and more.