Personalization of financial products with predictive analysis, why is it important?

Who hasn't dreamed about predicting the future? Throughout history, we have tried to achieve this by exploring all possible disciplines, from the scientific to the mystical. The reality is that today we are closer than ever to reveal this mystery, but we wouldn’t use a crystal ball, we use machine learning and big data.

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In this article we will see how technology is transforming financial processes and achieving a never-seen-before customer retention rate. Predictive analysis in finance has really interesting applications: from offering personalized banking services to forecasting the stock market thanks to AI.

Óscar Barba

Co-founder & CTO of Coinscrap Finance

What is predictive analysis and how is it used in finance?

Predictive analytics is like a digital psychic. It uses current and historical data to calculate the probability of a specific event or action occurring. Predictive analytics tools are based on statistics and techniques powered by artificial intelligence. These developments help financial institutions improve their results with valuable insights.

Now that we have seen what predictive analysis is,

Let’s look at some of its main applications in the world of finance:

  • Rotation prediction: Identify at-risk customers, discover why they might leave, and refine your retention strategy.

  • Smart recommendations: Offer personalized product suggestions to increase upselling and cross-selling, expanding the visibility of your products.

  • Demand outlook: Anticipate fluctuations in demand to improve processes and reduce costs.

  • Prediction of financial risks: Evaluate the solvency of your customers and collaborators to minimize the risk of losses.

  • Predictive segmentation: Predicting the likelihood of a customer to make repeat purchases, abandon shopping carts or stop purchasing, and send customized messages.

  • Price personalization: Optimize your pricing strategies and adjust rates in real time based on consumption patterns and market conditions.

  • Predictive maintenance: Predicts equipment failures with great precision to reduce breakdowns, increase productivity and reduce maintenance costs.

Disorganized data represents almost 80% of the information in the banking sector and is a puzzle that is impossible to solve without the right tools.”

Your entity needs to opt for hyper-personalization

Hyper-personalization in banking means adapting financial services to the specific needs of users in real time. This can be done by taking advantage of the data they generate daily, analyzing their behavior and taking advantage of the technology we have at our disposal. Let’s face it: there is no more differentiating factor in the market than digitalization.

Furthermore, in this digital world in which we operate, personalization is key to prevent your customers from looking for a better experience elsewhere. You should offer the same personalized attention that non-financial companies provide.

How can you create exceptional CX? I’ll tell you below:

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Take advantage of artificial intelligence and machine learning

Imagine your data as a hidden treasure waiting to be discovered. Artificial intelligence, machine learning, and deep learning are like treasure maps that guide you to hidden connections in transactional data. These reveal treasures of personalized knowledge, or rather, useful information to outline your business strategy.

With these three tools we can discover habits in customer behavior. And, thanks to them, fintech companies create mosaics with user data, so that, with a single glance, you discover quality insights with which to offer tailored products. Our own AI engine is expert at analyzing disorganized and chaotic data. This data represents almost 80% of the information in the banking sector and is a puzzle that is impossible to solve without the appropriate tools.

Deep learning algorithms predict what will happen in the future based on an ocean of information. Unlike manual approaches, this technology takes you deeper into the data, revealing connections and correlations that previously went unnoticed. They also allow you to address financial exclusion, giving more people access to your services.

If you are interested in predictive analytics in the financial sector, we have another article for you.

Predictive analytics use cases in banking and insurance

If you want to suggest solutions tailored to each customer’s individual needs, you can start by identifying opportunities. For example, if a group of users with similar annual income tends to spend more on travel than on financial products, machine learning models can discover this pattern. This will allow your bank or insurance company to offer cash back programs in hotels and apartments to these user profiles.

Also these tools help you anticipate fraudulent actions online. They can detect cyberattacks before they occur by identifying anomalies: subtle, unconventional types of behavior that humans would likely miss, but that deviate slightly from the norm and may indicate cybercrime.

Don’t forget that algorithms improve with experience, like experts honing their skills over time! As they process new data –even data they haven’t seen before– they adjust their models to quickly adapt to new situations.

This means that they can automatically evolve and update the detection rules without human intervention, maintaining a constant evolution that improves your results over time.

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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.

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