4 ways Artificial Intelligence is revolutionizing Fintech
Artificial Intelligence (AI) is powering financial institutions like never before. The role of AI in fintech is becoming more and more important by the day. Also, the benefits of AI in fintech are too many for financial institutions to ignore. Increasing financial and banking institutions are embracing the features of AI to make their process more precise and prompter. Moreover, AI and machine learning can process the huge amount of information about customers. It is often hard for executives to sift out what are most vital uses of AI in fintech. Below, we have identified the 4 most important ways in which Artificial Intelligence is revolutionizing fintech.
1. Fraud Detection
In the US alone, 70 billion USD is spent by banks on compliance, according to the Alan Turing institute which is a huge amount of money. In the fraud detection process using AI, first of all, it is made sure that enough data has been collected for analysis. After that, AI tools learn and do surveillance on the behavioral patterns of customers to recognize a rarity. A rarity is a sign that something suspicious might be happening. After the isolation of these suspicious activities, then it can be easily determined whether that was actually fraudulent activity or merely mistakes that made it through the approval workflow. In the claims management process as well, AI is able to spot patterns to identify any fraudulent claims in this process.
An example of a company successfully using AI in fraud detection is MasterCard. In order to analyze historical payments data from each customer to detect and prevent credit card fraud in real time, Decision Intelligence (DI) technology was launched by MasterCard. Also, Chinese e-commerce giant Alibaba is using Alipay – a fraud detection system using AI in the form of a customer chatbot.
2. Improving Customer Support
Financial well-being of people is the most important thing for them after their health. To improve customer support, Chatbots are a commonality in a lot of industries and are starting to gain their footing in the finance industry as well. For example, Ant Financial Chatbot system of Alibaba surpassed human performance in customer satisfaction in 2017. Also, 2-3 million user queries are handled by Alipay per day – Alibaba’s AI based customer service. The system completed 5 rounds of queries in one second, according to 2018 data.
It is useful for executives to know that human-like customer experience or expert advice experience can be delivered through customer facing systems like text chats, voice systems or finance chatbots. Fintech companies can save a great amount of time and money through the use of automation and chatbots. Amplification of the capacity and quality of traditional outbound customer support through Virtual Assistants helps smooth out internal operations. For instance, the burden of the Royal Bank of Scotland, which was over 30,000 customer service agents who had to ask between 650,000 and 700,000 questions every month, was reduced through the use of virtual assistant LogMeIn’s Bold360.
3. Credit Risk Evaluation
For companies utilizing AI in fintech for credit risk evaluation, AI provides a huge advantage. For investments and loans, underwriting services are offered by insurance firms. Instant assessment of a customer’s credit risk can be made possible by AI-based model which allows consultants to design the most suitable offer. As an example, a financial services group based in Canada called Manulife, is the first player to use AI for its underwriting services.
It has proven to be key to closing Canada’s protection gap by making purchase of basic life insurance quicker for Canadians. Moreover, Artificial Intelligence Decision Algorithm (AIDA) is a private organization employed by many insurance firms. It can have various classification processes such as compensation or high losses. Also, it is trained in past underwriting techniques and costs.
The success of AI in fintech is unquestionable. For instance, Lenddo, as software-based services company, uses an applicant’s complete digital footprint to generate credit risk score instead of relying only on loan application data. Furthermore, social media, browsing history, geolocation, and other external data are analyzed to form more concise judgements about credit risk. Because of this improved credit risk understanding, Lenddo claims to enhance some of their client’s credit approval rates by up to 50%.
In order to fund their distribution accounts in the right manner, make opportune decisions for borrowing or investing, maintain target balances and satisfy all regulatory requirements, accurate cash forecasting is a necessity for treasury professionals. It is a fantasy, however, to assume that 100% accurate forecasting will be achieved, because the data from internal ERPs is too complicated to standardize, centralize and digitize. Extracting any meaningful insight from this data, hence, remains even more far-fetched.
Machine learning, data mining and modelling to historical and real-time quantitative techniques is used in predictive analytics, in order to predict the future events and enhance cash forecast. Hidden patterns are picked up by AI which the humans cannot identify. For example, the repetitive characteristics of payments which consist of random sequences of letters and numbers. Furthermore, business trends to pull valuable insights, optimize business models, and forecasting a company’s activity are used by Actualize Consulting, which is one of the most advanced programs in predictive analytics.
Above mentioned are the most important uses of AI in fintech. There are numerous other uses as well. The executives need to assess ROI for their specific company and see if employing AI in their financial institution is worth it. There is no doubt, however, that the impact of AI in fintech is revolutionary. As new information and insights are rapidly emerging in the case of AI in fintech, executives will be well advised to keep reading articles like this one to stay updated on important developments in AI field.
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