One of the early adopters of relational databases and mainframe computers have been the financial firms who have been a pioneer at adopting the latest in technology. Artificial Intelligence (AI) has taken the FinTech industry by storm which has been assisting financial institutions in solving redundant issues and increased operational efficiency. In the last two decades, FinTech companies have revolutionized themselves by adopting technologies like AI, Machine Learning, Neural Networks, Big Data Analytics, evolutionary algorithms, to name a few that have allowed computers to crunch and process an immense variety of diverse and deep datasets than ever before.
There are a few important areas where AI is impacting in a game-changing manner for the FinTech industry. These include:
- Groundbreaking insights and analyzable predictions
Importantly, one of the most significant requirement of AI in the FinTech arena would be producing ideas that can precisely predict customer behavior. If we take for instance that there is an existence of AI, through which financial institutes can decipher a customers’ past behavior and give correct recommendations on the customers’ credit-worthiness. We can observe that the machine learning algorithms available today are not able to offer to the reason behind how a result was achieved. PwC has foresighted that by the end of this year, Explainable AI will be adopted by enterprises with open arms either as best practice or obligation, and governments may make it a regulatory requisite.
- Early sensing and prevention of cyber-security danger
For the FinTech’s security is a primary concern, particularly when it comes to online transactions. FinTech companies can build robust security systems by using Generative Adversarial Networks (GANs) into their solutions. The GAN works with two opposing networks:- one generator and one discriminator. The generator network makes fake data that can replicate the real data set. The discriminator network accesses both fake data and real data, and each network acquires from the other and gets better over time. This system can be particularly useful in detecting deceitful behavior, suspicious transactions and initial perception and prevention of cybersecurity threats.
- Visual determination and verification
Using capsule neural networks to ocularly determine customers and documents could offer a huge leap in contouring functions like a loan and insurance origination, account creation and documentation. An AI could visually corroborate if the documents uploaded on it are reliable, and whether a customer is trying to go for a loan is the person who he or she claims to be. AI could also offer a huge degree of automation conjugated with a credit admin software that takes care of loan documentation for all parties which were previously thought impossible.
- Bots that are incorporated with human features
An AI could power revolutionary chat-bots coupled with unique customer engagement platform that can instantly and uniquely answer customer questions, efficaciously reducing load from customer services department. These chatbots can be incorporated with social networking sites, and accept requests for application and orders straight from social media channels. Gartner foresees that by 2018, more than 2 Bn people will be regularly using conversational AI to communicate with virtual customer assistants on smartphones or through connected devices.
- Fraud spotting and Claims Management
AI tools can learn and monitor a user’s behavioral pattern once analytics tools gather evidence and analyze data which is required for a conviction to identify rarity and warning signs of fraud effort and occurrence. Claims management can be build up using machine learning techniques in various stages of the claims handling execution. By leveraging AI and processing an immense amount of data in a less period, insurers can automate handling mechanism. It can even speed up specific claims, to decrease the overall processing time and also the handling charges while increasing customer experience. These algorithms recognize patterns in the data to help acknowledge fraudulent claims in the process. With their self-learning abilities, AI systems can then adapt to new unexplored cases and further develop the detection over time.
