There’s no question that technology is changing the face of finance. In fact, former Citigroup CEO Vikram Pandit said that technology could replace approximately 30% of banking jobs by as early as 2023. A huge portion of this staggering change can be attributed to the widespread use of big data, particularly in the growing field of predictive analytics.
A relatively new branch of data science, predictive analytics has been especially useful for fintech companies that rely heavily on data collection and finance trends. Special Counsel points out that the science behind predictive analytics uses a variety of machine learning (ML) techniques, in addition to data mining, computer science, and artificial intelligence (AI). These techniques are applied to fintech solutions such as blockchain, crowdfunding, mobile payments, and much more. So, here is a list of some of the ways predictive analytics is transforming the fintech industry.
Preventing Cyber Fraud
One of the biggest and most common applications of predictive analytics is in strengthening cyber security efforts and preventing fraud. Fraud through cyber attacks is becoming a major problem in banking, given the increase in digital transactions and the rise of automation. As a response to this growing threat, international banking giants like HSBC and Standard Chartered Bank have entered into collaborations with fintech companies to help them develop AI and ML solutions to fight financial crimes.
By combing through millions of data points from user transactions, predictive analytics is able to discover and record patterns and routine behaviors. Any deviation from these patterns will send bank officers an alert for possible fraudulent activities, even before users file a report or complaint. These processes, made possible by predictive analytics, result in stronger cyber security systems and strategies in the finance industry as a whole, but especially in fintech solutions like bitcoin, blockchain, and mobile payments. Even insurtech companies are now using predictive analytics for their risk analysis algorithms, as it is an efficient way to screen potential borrowers.
Better User Experience
Fintech companies are finding themselves in a tremendously cutthroat market with stiff competition. That being said, customer retention through excellent user experience and lead conversion rates are now top priorities for those who want to stay ahead. Predictive analytics plays a big role in the strategies necessary for these, with ML and AI proving to be extremely valuable in these areas.
A great example of the prevalent use of AI and ML is the use of chatbots. Though they may seem simple from the surface, chatbots are actually a powerful force that more and more businesses are beginning to embrace. Indeed, a global market review on Small Business Trends reveals that the worldwide chatbots market will be worth a whopping $1.25 billion by 2025, and for two good reasons. First, chatbots allow companies to respond to customers 24/7, eliminating both downtime and the need for excessive customer relations employees. Second, the AI that enables chatbots to collect consumer data can then use the data for pattern analysis. These analyses ultimately lead to more precise and nuanced insights into customer behavior and preferences, which allows fintech companies to tailor-fit financial products and services.
Overall, the combination of predictive analytics and fintech solutions creates a safer, more efficient, and competitive finance industry. The current cycle of continuous learning through AI and ML will undoubtedly lead to more exciting improvements in the near future. For both the average consumer and the industry, this means more innovative ways for faster and easier transactions, with less and less risk of cyber fraud or theft.
