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Big Data and Banking: the Value of Machine Learning

The financial sector has experienced in recent years important growth and integration processes, a continuous and accelerated incorporation of new technologies to the business, and the universal deployment of the concept of multichannel.

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Big Data and Banking

The financial sector has experienced in recent years important growth and integration processes, a continuous and accelerated incorporation of new technologies to the business, and the universal deployment of the concept of multichannel (any operation, from any channel, at any time).

As a result of these factors, modern financial institutions handle huge volumes of information from their customers, what we usually call Big Data. The techniques of analysis of this information also evolve from the “ex-post” analysis, which allows to understand past behaviors to draw conclusions, towards a preventive analysis, which allows predicting and, why not, what is most important for the business: manage in advance what has to happen.

Compared to traditional statistical modeling, which finds basically linear relationships between a limited number of variables, Machine Learning techniques allow the discovery of hidden patterns in millions of data with even non-linear relationships. In addition, you can create systems that learn and automatically adapt to new patterns discovered.

Machine Learning techniques have a huge application in the financial sector, in different areas:

  • Business: detect patterns of behavior in customer data allows a much greater knowledge of them, to design and offer more specifically customized solutions.
  • Risk: the advanced analysis of independent variables in the financial statements provides valuable information about the behavior of default levels in credit risk.
  • Fraud: early detection is based on the discovery and automatic detection of associations and rules that can mean interesting patterns; in the creation of expert systems to codify the experience; in the recognition of classes, clusters or suspicious behavior patterns; in machine learning techniques to identify such patterns, etc.
  • Efficiency: the automatic identification of patterns of behavior can contribute to the most efficient use of resources, such as in the call center services, in the forecast of breakdowns, etc.

In the Financial Sector, Machine Learning solves the following issues:

  • Advanced segmentation of clients, according to their behavior.
  • Definition of spending / saving profiles.
  • Definition of the optimal product portfolio for each segment.
  • Cross sale of personalized products and services.
  • Personalized recommendation
  • Increase in LTV (Lifetime Value).
  • Prevention of customer flight.
  • Improvement in risk concession decisions.
  • Early detection of delinquency, optimization of credit risk.
  • Detection and prevention of fraud.
  • Prevention of queries and incidents, and acceleration in their resolution.

Conclusion

The financial sector is being strongly affected by the so-called Digital Transformation, which has its epicenter in the advanced processing of data. It is common to find financial institutions that try to store a lot of data, but few of them are gaining true value to them, in a consistent way.

Machine Learning is currently the most efficient tool to extract the juice of Big Data. It also implies a paradigm shift in the programming systems. It represents a significant leap that leads to static programs (identical responses to equal entries) new scenarios in which the programs learn autonomously over time (and therefore offer different answers depending on what they are learning).

The examples indicated in this article are a small sample of the existing possibilities. Joint work between business areas and data scientists allows us to approach new approaches and apply truly transformative solutions.

Big Data and Banking: the Value of Machine Learning
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