During the earlier part of the 21st century, Data Science was primarily based on theoretical concepts that lacked practical applicability in our daily lives. However, as we started to have the availability of faster processing power, high-speed internet, and multiple storage options, it helped immensely in transforming the theoretical concepts of Data Science into reality. Nowadays, learning the skills needed to be a successful professional in the Data Science domain is important more than ever as it has become easier for the related professionals to not only cleaning, wrangling, transforming, modelling, and even visualizing billions of rows of data efficiently and effectively.
In the first part, we will look into some common applications of Data Science in the industry of Finance:
Surprisingly, the earliest applications of Data Science were not in the domain of Information Technology but in the industry of Finance. It shows how critical Data Science has always been for this industry.
Customer Data Management:
Previously, any data related to customers was considered irrelevant to the organization and was not even considered useful as there were not any tools or algorithms to extract insights from the data. However, banks have now realized the importance of the data as it is not only used for analysing the trends of transactions, groups from where the customers belong, and is even helping them immensely offering the right products and services which the Data Science and Machine Learning algorithms suggests them over a predefined course of time.
For any organization, there are multiple risks associated with the competitors, financial loss, losing customers, and even future strategies for expansion of the business. Especially for financial institutions like banks and insurance companies, risk aversion is one of the key parts of running the business successfully.
Nowadays, risk aversion practices in these financial institutions are dependent on the advancements of Data Science and Machine Learning. It enables the companies to analyse the past customer and transactional data and groups them under different levels of threats so that the strategists can take feasible decisions based on the data presented to them.
Fraud and Risk Detection:
In the past, banks always had huge chunks of financial data of bad debts and losses annually and it was impossible for analysing the data to minimize the losses and expand the profits as much as possible. However, with the progress of Data Science and Machine Learning in the financial services industry, it has become a reality for the bankers to transform the banking industry profitable again by application of suitable algorithms like Classification, which analyses the pattern of historical transactions and on the basis of that, predicts whether the newer transactions can be classified under the umbrella of fraud or illegal transactions or not.
Personalized services are becoming more and more relevant in today’s business world. The advancements and applications in various algorithms and technologies have enabled the companies of implementing them on the application’s UI being provided to the customers.
This has proved to be effective for financial institutions as well.
Although the data being used for providing personalized services may differ from industry to industry, generally the types of data which is analysed as far as financial industries are concerned are mentioned below:
- Bank Balance
- Repayment of loans
- Shopping history
Apart from providing personalized services, the related algorithms provide a credit score related to every customer. It enables the marketers for offering products and services to the customers related to that particular credit score.
Automation of Credit applications:
The process of automation has revolutionized almost every business domain around the world. Similarly, financial institutions have automated several processes that were manually processed in the past.
Previously, banks and insurance companies have had difficulties in the implementation of transparencies and standards in process of applications which resulted in huge losses in terms of bad debts for the financial companies.
However, Data Science has contributed to maintaining the standards as far as credit applications are concerned by the application of algorithms like clustering in assessing the applications, which has resulted in a decrease in the number of yearly losses for them