The financial sector majorly benefits from the technological transformation. Although the financial sector is strictly regulated, the Fintech sector has been thriving since the emergence of smaller startups and continues to seek disruption in the space by using the latest tech such as data science. Furthermore, the sector has developed many quantitative analysts who use programming techniques and quantitative analysis to exploit huge opportunities present in the capital markets. The following are ways in which data science is transforming the Fintech sector.
Detection and Prevention of Fraud
Financial institutions are greatly affected by fraud cases. Although the sector is highly regulated, they still experience criminal activities leading to huge losses. Both individuals and institutions encounter the impact of fraud; hence fraud detection is a major concern for Fintech directors and Cane Bay Partners. Currently, fraud is based on particular rules such as flags triggered based on ATM, location, or IP address applied.
However, rather than relying on limited transaction numbers, the processes may be enhanced by applying machine learning techniques like naïve Bayes classifier and logistic regression. This technique can compute a transaction’s probability of being deceitful according to the patterns in the previous transaction data. Since this enhances accuracy and can be adopted on live data, it can help Fintech institutions to take effective actions.
Credit Risk Scoring
A credit score refers to statistical scrutiny used to predict an individual’s creditworthiness according to their past credit information. This analysis determines whether a loan should be granted to an individual or not. Typically, financial institutions use complex statistical ways to determine a person’s credit score. Fortunately, the emergence of data science has facilitated innovative techniques like machine learning algorithms that offer evaluations at an advanced accuracy by applying for large data point numbers. Data science is used to provide a complete view of an individual’s creditworthiness by considering their holistic data.
For instance, some companies like Alibaba (Aliloan) use automated online systems to offer flexible microloans to online business operators, which traditional financial institutions overlook since they lack collateral. These systems gather data from payment and e-commerce platforms and apply predictive models to evaluate customer ratings, shipping records, transaction records, and other information to determine the entrepreneur’s creditworthiness.
Asset Management Portfolio Optimization
Asset management and portfolio optimization are essential roles undertaken by Fintech institutions. With the emergence of Big Data, these companies may crunch a huge quantity of financial information to develop asset management according to machine learning codes. This has facilitated the rise of Robo-advisors, whereby firms such as Wealthfront and Betterment apply software in automating asset distribution decisions used to minimize risks, offer automatic tax-loss harvesting, and enhance returns.
Sentiment Analysis is also applied to analyzing public information like Worldwide Twitter Feeds to sort the market and measure sentiment when an artificial or natural disaster occurs. This procedure can be completely automated, hence reducing the general costs of these organizations.
Customer Retention, Marketing, and Loyalty Programs
Fintech firms gather large data chunks from their clients. Typically, this data stays unused unless when relevant for financial evaluation. However, this client data, which comprises their transactions and their details plus social media presence, may enhance a company’s marketing determination. This is done by providing discounts and personalized plus contextual product advertisements, helping create awareness, customer retention, and improving sales. Moreover, these details may be used to target prospective clients, hence helping to optimize the firm’s marketing strategies.
Today, the world is undergoing tremendous changes in terms of technology. The financial sector has greatly embraced technology, hence enhancing efficiency and loss prevention. Besides, adopting data science can positively influence an organization’s profits and growth.