The entire financial service industry has been taking advantage of big data for some years now. Technological advancement is really changing how people handle transactions. Fintech basically comprises of companies that maximize the latest technology to boost their financial systems. Top financial companies and brick-and-mortar banks are enjoying the wide range of solutions that it offers. Startups all over the world now provide financial technology that include wealth management and alternative lending. Unlike before, small lenders no longer find it difficult to secure loans.
Without mincing words, fintech companies have encountered different challenges in the past, especially funding to facilitate expansion. Things are changing as the sector is currently experiencing an exponential growth rate. It was recently reported that the global investment in the industry reached a staggering $23.2 billion in 2016 alone. This was made possible by the adoption of innovations in departments like financial literacy, retail banking and investment. The competition is getting stiffer due to the fact that different companies are now delving into it.
One of the primary benefits that big data offers to fintech enterprises is predictive analysis. It is a tool for setting very precise borrowing terms and can help to minimize the chances of dealing with risk borrowers. Another benefit is that it helps brands to carry out internal audits in order to meet the compliance standards of financial regulators. Fintech companies can integrate big data analytics in the following ways.
How Big Data Analytics Can Help Your Business
Big data analytics is a tool for determining the type of extra services that a particular client would be interested in. Public and internal data can come in handy for creating robust customer profiles. Sectioning the target audience based on relevant parameters is a sure way to attract more customers and build customized offers. The cost of acquisition will be greatly reduced once digital channels are employed.
Fintech enterprises should be poised to deliver quality services to their clients under any circumstances. Today’s customers are ready to share their data as long as it’s protected from any third party and used to improve the range of services provided. It’s no surprise that big data analytics and AI algorithm are being trained to generate lots of ideas. The outcome is useful for enhancing the overall customer experience. There is no need for agents anymore as AI driven robotic advisors that offer financial advice online are on the rise. Even though public data is easily accessible, it doesn’t make any difference until it is mined and analyzed by experts. Later on, customers’ behavior can be revealed. This goes a long way in releasing products or services based on consumer needs, complaints and preferences.
Wearables, IOT, mobile technology and cloud computing are effective means to garner data and retain customers. Social media data is also a reliable source of valuable insights. Personalized products, services, discounts and other incentives are widely used to increase engagement, leading to an improved ROI. Fintech stakeholders are busy maximizing strategies for retaining existing customers through marketing campaigns that include loyalty programs. As a result, it’s easier to achieve both long and short term goals.
Credit scoring involves examining the business operations of a financial organization in order to assign an appropriate credit score. Before the adoption of big data analytics and AI, the process relied on basic financial transactions. The scores were then used for all credit activities. In this era, other factors such as customers’ ability and behavior are duly considered.
Customers’ data must be kept secure by small financial institution and the established fintech companies at large. These organizations are expected to take proactive measures for protecting themselves and clients from fraud. Predictive analysis is gradually becoming a viable tool for minimizing fraud risk. The process involves supplying the algorithm with unprocessed data and training it to detect irregular patterns. Predictive analysis that make use of biometrics and device identification is gaining popularity for risk management solution.
Some of the fintech trends that were experienced last year will still be relevant, but in a better way. It has been predicted that more financial institutions will start relying on advanced blockchain software for handling digital payment. Other trends to expect is the collaboration between traditional banks and fintech enterprises and the use of wearables for managing digital transactions. It’s possible for financial institutions to come up with more creative solutions when they integrate big data with AI. This will in turn simplify the whole process and minimize risks in the sector.