The following piece is co-written with our friends at Kabbage.
The application of computation and statistics to the real world has opened up an entire new paradigm. Named data science, it is in the process of transforming everything from media to business into more efficient and productive endeavors.
Nowhere has data science been more effective in creating change than the in the financial world. Data science has spawned an entirely new financial animal known as fintech. Fintech has radically altered the financial landscape by facilitating the application of big data and complex calculations to financial decision making. Accuracy and predictive ability has been drastically improved across the board by the use of data science in the field of financial decision making.
At The Data Incubator, we work with hundreds of companies innovating in fintech and beyond who are looking to enroll their employees in our big data corporate training or hire data scientists who graduated from our fellowship. At Kabbage, we are dedicated to supporting the small business community and helping small business owners qualify for a line of credit in minutes with our fully automated online lending platform. Below are five applications of data science in Fintech.
The weighing and evaluation of risk is a key tenant of finance. Risk evaluation travels across the financial spectrum from creating online working capital loans to making investment decisions. Data science is the backbone that allows fintech to build quicker and more precise credit risk decision processes than could ever be imagined in traditional institutions.
The preciseness of the evaluation opens up an entirely new client base while at the same time sharply lowers credit risk. Data science allows the online capital lender and others to accurately determine the creditworthiness of an individual by evaluating 15,000 data points. Items as unexpected as application typing speed and word usage are used along with the traditional data like credit score to build a credit risk model. Prior to the creation of data science, using anything beyond the traditional loan risk evaluation models would have been impossible.
Payments and Purchasing Habits
Data science allows a customer’s payment history and purchase history to be evaluated at the granular level. Granular evaluation opens the door for precise prediction models as to what behavior to expect going forward.
This evaluation can vary from basic analytical scores built on volume of spending, from month to month to more complex calculations such as the use of payment records and spending habits to target marketing, loyalty rewards, and other forms of active customer interfacing.
Determining Lifetime Customer Value
Data science allows fintech firms to drill into and clarify the lifetime value of every customer. Rather than just viewing customers as one time transactions, the application of fintech allows the entire potential lifetime purchase volume to be evaluated.
The evaluation of the potential lifetime value creates the opportunity for upselling and targeted marketing based on where the customer is on the model. Fintech can utilize metrics as diverse as social media feeds to direct feedback via surveys to build a lifetime value model. Understanding the lifetime value of a customer permits the correct application of resources on the customers most likely to be of high value into the future.
Data science has provided major institutions the power to crunch massive amounts of data to build asset management models to earn higher risk adjusted returns for their clients.
Today, the application of data science to fintech has created the new wave of robo-advisors geared toward the individual investor. Robo-advisers remove the emotion inherent in the human decision making process. In addition, robo-advisors’ decision making process takes into consideration data points and historical trends to make scientifically sound asset allocation decisions across the spectrum of investable assets. This diversification provides lower risk along with improved odds of a market-beating investment.
Fraud Detection and Prevention
Data science radically improves the process of fraud detection and prevention. The ability to monitor transactions in real time and flag the ones that fall outside of the average is a powerful tool in the war against fraud.
Fraud prevention is among the top priorities of fintech firms, therefore many resources have been focused in this direction. Early warning systems have been designed by utilizing data science that are uncanny in their predictive ability. There are a plethora of new fintech firms whose sole purpose is to provide fraud protection services to other fintech firms.
Without data science, fintech could simply not exist. Data science is how fintech firms make decisions about everything from asset allocation to online working capital loans. Data science has truly revolutionized the business world!
Michael Li is the founder of The Data Incubator, a Big Data training company that offers Corporate Training and hiring solutions. He has worked as a data scientist at Foursquare, Google, Andreessen Horowitz, J.P. Morgan, and D.E. Shaw and is a regular contributor to Harvard Business Review, O’Reilly, and VentureBeat.
Jess Harris, Head of Social Media & Content Marketing at Kabbage, Inc., has been helping small brands and startups expand their brand presence online for the last 8 years. Jess particularly loves helping small businesses start from scratch, using actionable insights to build a solid digital media strategy.