Leading financial companies and banks have always been at the forefront of adopting new technologies. The emergence of a fully fledged technology niche called “fintech” itself proves how close a relationship the financial sector holds with the modern technologies. From net banking to mobile banking to personalised banking, it has been a long journey dotted with innovative technologies and unique tech ideas crafted for financial companies.
While developing mobile apps for banking and contactless financial transactions became the norm for the financial sector, leading fintech companies made a competitive leap by embracing a range of latest technologies such as Blockchain, Artificial Intelligence and Machine Learning. Both artificial intelligence (AI) and machine learning (ML) intertwined with one another opened a breathtaking new scope to serve customers better, take decisions more diligently and establish better brand credibility.
know that early adopters of mainframe computers and relational databases have been Fintech companies. They were always keen to understand how technology can smoothly solve human problems, thus increasing the companies’ efficiency. These companies started adopting methods that included AI and Machine learning that was derived from various aspects of human intelligence. Varied, deep, and diverse datasets can be crunched easily by using these technologies.
No wonder, AI adoption is experiencing exponential growth in fintech companies all over the globe. According to latest research and estimation, the global AI in the Fintech market stood at around USD 7.91 billion last year and is expected to grow and reach USD 26.67 billion by the year 2026.
Here throughout this post we are going to have a closer look at the most popular AI use cases in the fintech sector.
Fraud control and compliance management
The concerns over cyber security in FinTech is constantly on the rise and AI has emerged as the technology solution to deal with cyber threats. The most important use case of AI in the fintech sector is fraud detection, fraud control and compliance management. A latest study by Alan Turing Institute mentions that banks in the US spend $70 billion USD every year on compliance. On the other hand, there has been a whopping 66% increase of frauds related to payments and transactions in the UK between 2015 and 2016.
AI offers a truly paradigm shifting technology in the worldwide efforts in curbing and controlling financial frauds. Machine Learning (ML), a subset of artificial intelligence technology is used in creating algorithms that can easily analyze tons of data points within seconds and detect anomalies in different transaction patterns. These algorithms based on several factors can easily classify suspicious activities from the so called data errors.
Mastercard to prevent credit card frauds are now utilising historical payment analysis measures. Data analytics companies like Data Advisor are now utilising AI to prevent manipulation and exploitation of credit card sign up bonuses for new accounts. The Chinese retail giant has come up with AI based fraud detection chatbot called Alipay. Already Capgemini, Teradata and Datavisor have come up with intelligent fraud detection mechanisms based on AI. Their solutions are now being widely used by leading banks and financial institutions.
Decision Intelligence: AI powered accuracy in decision making
Financial sector is prone to major risks like market volatility, gross negative impact of policy decisions and insecurity in the international monetary market. To prevent exposure to these risks and volatility, it is imperative for the financial firms and banks to stay ahead of the competitive curve with data driven decisions. Staying ready for the impending crisis is what every financial company strives to ensure.
When it comes to turning raw data into better actionable decisions, AI and ML technologies can really play a definitive role. AI and ML technologies have the bigger capacity to deliver more powerful data driven insights for financial decision making. This empowered decision making based on AI technology is widely being called as Decision Intelligence, a new AI frontier for fintech companies.
How decision intelligence really works for fintech companies?
Thanks to AI powered decision making, financial firms can take the optimal decision spearheading growth. The capability of machines in dealing with bigger volumes of data compared to humans ensures more accuracy and precision. In the context of financial companies, rummaging larger volumes of data through machine capabilities ensures better understanding of market risks, volatility in interest rates and the unfolding market opportunities.
To understand how AI-driven decision intelligence is really making an impact on financial firms and banks, let's explore some great use cases related to the financial industry.
Decision making for financial investment and asset management:
Thanks to AI powered decision making, investors can have a better strategy for fund hedging, investment portfolio building and real time trading decisions based on data driven insights. Morgan Stanley came with a predictive analytics tool called WealthDesk through which A.I. can help investors in decision making.
Banks catering to general and business customers through retail banking solutions can get great help from Artificial Intelligence (AI) for staying ahead of the competition. AI powered decision Intelligence can ensure optimised pricing and interest, data driven marketing of financial products and advanced personalisation and segmentation for more customer centric services.
Decision Intelligence for payments and transactions:
For banking payments, AI based inputs can be utilised for enhancing security based on contexts and possible vulnerabilities. Mastercard’s Decision Intelligence solution can be taken as a great example of how A.I. can be used to adjust security for every transaction.
Optimising Customer Support
For the financial well being of the customers, providing great customer service is a non-negotiate necessity. From intelligent and adaptive chatbots to A.I. powered algorithms and tools for detecting customer pain points in real time, AI has already made a great impact on the customer support of the banks and financial services. In spite of the enhanced cost of app development, fintech apps are taking advantages of AI and intelligent customer service automation through chatbots.
If you think A.I. powered chatbots still lag behind human support in terms of intelligence, you should know about the chatbot system of Ant Financial, an Alibaba company. This chatbot system is claimed to have exceeded human capacity and capabilities in ensuring customer satisfaction.
Besides the increasing capacity of the intelligent chatbot systems across companies, the role of the human support team in amplifying the precision and accuracy should also be considered alongside.
AI powered data driven decision making can allow fintech companies to target their customers with products and services based on their likelihood to buy and opt for. By analysing customers through data driven insights fintech companies segregate customers in precise segments based on their demographics, purchasing power, employment, investment capacity, financial strength and online behaviour.
Thanks to A.I. powered and data driven revision in segmenting customers, fintech companies can also personalised their offerings for each individual customer through prior assessment. Since a whopping 76% of customers now want their needs and expectations to be addressed by companies with specific value offerings, data driven personalisation plays an important role.
All the statistics, use cases and examples point to the huge role played by A.I. and ML technologies in converting business, pushing the growth of financial services and optimising the service excellence for end user experience. No doubt, AI already played an era defining impact on the financial industry and helped banks and fintech companies create a plethora of opportunities with the promise of a win-win situation for both bankers and the customers.