Data Analytics applied to the financial sector

The role of analytics in companies, thanks to the rise of the Internet and new technologies has become more relevant in different sectors in recent years. Likewise the analysis of the data collected which a few years ago took a long time to be able to carry out actions can now be achieved in real time with advanced analytics.

Our consulting unit provides advanced analytics services frequently started with programming the predictive model according to certain algorithms or variables that are relevant for the analysis and in this way the system upon receiving information and data in real time will be able to generate an immediate projection that will serve as the basis for making decisions in this regard.

On the other hand the advancement of technology has not only allowed the immediacy of data analysis due to machine learning and faster decision making but now there are more storage options for the data collected. In additio in recent years the growth of cloud services that provide extensive repositories, interconnectivity and other elements of Big Data allow processes such as the 4V’s (speed, data variety, volatility and visibility) to be more efficient.

One application of this technology in the financial sector is the risk model at the moment a client wishes to access a loan and by analyzing the level of indebtedness and other variables it is possible to identify a prospective client who will become delinquent over time so that the financial institution based on this information can make a much more immediate decision on the possibility of providing a loan.

Another contribution of advanced analytics to the financial sector is applied in the predictive model to detect fraud in which by analyzing and learning patterns of repetitive behavior in customers when a movement occurs that is not consistent with the pattern already defined the transaction can be blocked until it is verified with the customer.

Similarly it also allows for a more precise segmentation of customers or markets by obtaining and detecting user behavior patterns. With this actions such as promotions and loyalty campaigns can be carried out thanks to the analysis of this data as well as anticipating a possible abandonment. Moreover by obtaining more accurate information and data it is also possible to develop products with a higher level of personalization for current customers and also to locate potential customers for companies in this industry.

Because of this today new financial institutions such as savings and credit banks are also incorporating these technologies which in addition to the benefits already mentioned allow the creation of new business opportunities by performing a correct analysis of user data these entities can take advantage of the characteristics that distinguish them from large banks in addition to having a better risk management.

Therefore applied analytics has become a vital aspect for any financial institution and the way in which it is carried out will not only allow timely decisions to be made but can also serve as a basis for generating solutions to serve its customers.