Data Science has played a significant role in transforming the finance and banking industry by completely changing the ways in which they previously operated. Life has been made easier for the banking officials as well as the customers. FinTech: a new term coined for the innovation and technology methods aiming to transform traditional methods of finance with data science forming one of its integral component. Whenever you use your credit card, Amazon Pay, PayPal, or PayTm to make an online payment, the commerce company/seller and your bank, both utilize FinTech to make a successful transaction. With time FinTech has changed almost each and every aspect of the financial services, which includes investments, insurance, payments, cryptocurrencies and much more. Fintech companies are heavily dependent on the insights offered by machine learning, artificial intelligence and predictive analytics to function properly.
In this post, we will take a gander at the manners by which data science is fundamentally altering the FinTech innovation and helping it to work proficiently.
With an aim to make “credit accessible to more number of people”, FinTech companies use robust machine learning algorithms to predict the creditworthiness of people. This lets them reach a wider customer base and reduce the rate of credit defaults. Traditionally, banks use very complex statistical methods to determine the credit score of an individual, but with the help of data science, the good and bad borrowers can be separated in a fraction of seconds. In order to accomplish this task, a large number of data points are utilized by the companies. Also, all the data that is collected is further used to train the machine and improve its performance. Therefore, data science provides a holistic view of one’s creditworthiness. Companies like Alibaba’s Aliloan is an automated online system that provides small loans to entrepreneurs who otherwise would have been rejected by the banks because they have no collateral against which the loans could be given. This automated system collects information such as online transactions, business performance, ratings from the customers and much more to calculate the creditworthiness of the business owner.
Fraud detection and prevention has always been a top priority for the FinTech companies. At present, it is estimated that financial institutions lose about $80 million every year due to fraudulent activities. With the evolution of data science, the ways to detect fraudulent activities have also changed. Machine learning based algorithms are able to detect fraudulent activities better than the traditional systems that may sometimes even produce false positives and classify a normal transaction as a fraud as well. The advanced fraud detection systems work on supervised and unsupervised machine learning (ML) algorithms. Supervised ML-based systems are fed with historical data that has been labelled as fraudulent and non-fraudulent. This data set helps the system to classify any ongoing transaction as normal or anomalous. On the other hand, the unsupervised ML-based systems are just fed with a large amount of data that has not been previously classified, the system uses this data as a training set and learns to differentiate between standard and a fraudulent activity on the basis of transactions happening in digital space every day.
Fintech companies collect a large amount of data from their customers which is often used by them for financial analysis. This information can likewise be utilized for enhancing client base and expanding their lifetime value. Customer data right from their transactions, social media engagement, and personal information can be taken into consideration and used to offer them a better experience. For instance, by analysing the previous products purchased by the customers’ algorithms can be created to predict their future choices. These bits of knowledge can also be utilized to comprehend that what sort of items must be promoted among various age groups. FinTech companies may utilize client information to make thorough profiles of their clients and offer them a customized program for a superior ordeal.
One of the biggest challenges faced by FinTech institutions is to be able to collect revenue in timely and transparent manner. Predictive analysis and machine learning algorithms can be used to profile customers and use the insights to create optimal revenue collection strategies. Right from credit application scoring to calling indebted individuals to pay the obligation, machine learning proves to be useful. The calculations enable a monetary foundation to monitor their clients’ activities and subsequently discover the right time to make them a manual call for reimbursing the obligation. These techniques not just allow the foundations to spare a considerable measure of time yet in addition keep them from wasting their monetary assets.
Asset management and portfolio optimization (taking investment decisions) are significant elements of Fintech Institutions. The massive amount of big data collected by these firms can be used to construct machine learning based asset management models. This idea has additionally offered ascend to what we call as Robo-advisors, which are fundamentally robots helping the organizations to take their asset allocation decisions. This mitigates monetary dangers as well as enhances degree of profitability for the companies.
Data Science offers endless advantages to the fast evolving FinTech institutions. Money and its management has always been a concern for everyone, no business ever wants to face loss. Therefore, it is prerequisite that the market is analysed and understood in an efficient manner. The firms that will be able to gain these insights will definitely outperform the others in their respective fields. At DataToBiz the professionals have the skill to convert your business problem into the form that can be resolved by data science. After a thorough understanding of the issue that needs to be administered, the experts can devise algorithms to help you overcome the business & finance problems. They have an aptitude in machine learning and artificial intelligence that allows them to create optimal products as per your requirements. For more information regarding the services contact