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The Future of Artificial Intelligence in Fintech

31 July 2019

Artificial intelligence and machine learning are redefining businesses and industries across the globe by introducing technology solutions driven by innovation. People do not have to wait long to see the impact of AI in their lives; it is already in existence. From AI based trading systems to smart chatbots, technology has provided a viable solution which has reduced complexities in processes and made the outcomes less dependent on stringent formulae.

With AI integrations, the system learns on the go and adapts to the nature of the application in which it is used. The most significant aspect of AI is its learning potential and the way the algorithms help to modify the strategy / output based on external as well as internal elements. AI is also capable of eliminating human errors which makes it the perfect component in any application in the financial industry. Financial institutions are evaluating and adopting AI for protection against threats, fraud analysis, and investigation intelligence. With the rise of AI and further enhancements in technology, Fintech will witness major changes across all processes, especially in stock market trading.

Key Applications of AI in Fintech

Over the years, the evolution of AI has also made an impact on the services or applications provided by Fintech. It is interesting to see the domains and areas which are getting the benefit of artificial intelligence and machine learning. The following are some of the key areas where AI is making a significant impact with respect to FinTech.

Enhanced Customer Services

This is the most obvious and easiest integration of AI when it comes to FinTech. The use of smart chatbots provides the means to cut down the cost of helpline and front office staff significantly while ensuring optimum productivity and customer engagement. The key component of this integration is NLP (Natural Language Processing) which makes use of deep learning algorithms and generates responses which are more natural in structure and is similar to a real human conversation. However, the technology will take at least a decade (if not more) to become capable of replacing human resources completely. Some financial institutions have shared that there is a high probability of chatbots handling 80% of their customer interactions by the year 2020.

Improved Speed and Reliability of Credit Scores

With AI integration, it will be possible for data from social media, web browser history, geo-locations, and other sources be obtained and analysed to determine the credibility of a person with respect to his or her financial position. This is exceptionally useful for people with no significant credit history. For banks, this is a great way to assess the credit score of such underserved potential customers. The use of social media data for profiling people and assessing their credit score is more prevalent in the developing countries.

Fraud Detection & Claims Management

With respect to fraud detection and claims management, there is a lot of importance given to data and analytics. The analytics collects data from various sources but does not provide any pattern analysis. This is where AI comes in handy. The AI component learns and monitors user’s behavioural patterns to identify rarity and warning signs of fraud attempts and incidences. Machine learning techniques can be applied in different phase to reduce the time and cost associated with the claims management process. Due to the self-learning ability, AI algorithms are great for fraud detection with the use of behavioural patterns.

Automated Virtual Financial Assistants

Automated virtual financial assistants are driven by AI and use algorithms for monitoring events, stock and bond price trends according to the user’s financial goals and personal portfolio. The virtual assistants also provide recommendations with respect to making trades in the stock market. A better and more commonly used term for such assistants is ‘Robo Advisor’ which should not be confused with Robo Traders that make trades in addition to evaluating the market trends. As per current market scenario, Fintech companies are focusing on creating these Robo Advisors which tend to give an edge to modern investors and stock market traders.

Regulatory Compliance

Although conventional AI provides a solution wherein the data is analysed and predictions / calculations are made, there is no explanation of the logic behind the decisions made by the system. However, with the use of ‘Explainable AI’, it would now be possible to get the logic behind the decisions. For example, if a loan application is rejected by an AI system, then the ‘Explainable AI’ would provide the reasoning behind this action.

Predictive Analysis

Predictive analysis is a game changes for any business. When it comes to financial services, it can directly affect overall business strategy, sales nurturing, revenue generation and resource optimization. Fundamentally, predictive analysis makes use of a massive amount of data and makes predictions or suggestions based on trend identification and pattern analysis. The AI component helps to learn from the findings and make improvement to the quality of the predictions. For financial services which involve multiple transactions, details, and statistics, the availability of predictive analysis can save time, cost and efforts in various processes.

Data Security

A 2017 report by LexisNexis suggests that every dollar of fraud, companies have to spend $3.37 to resolve the problem and appease the customer. Innovation in security solutions has become the need of the hour. With each passing day, the amount of data available increases manifold and machine learning has the potential to provide the means to identify, analyse and implement the required security measures. Fintech companies are designing security solutions based on predictive analysis models wherein the system predicts certain outcomes (security breaches) and appropriate actions can be taken to prevent them.

Algorithmic Trading & Recommendations

Stock market is very dynamic and volatile. Slight changes in the market can lead to significant outcomes and it becomes challenging for people to keep up with the trends, back test the information, and make appropriate analysis. Artificial intelligence and machine learning algorithms are more than capable of processing huge volumes of data and provide recommendations. The only significant requirement is to have enough data for back testing. This will allow the algorithms to analyse historic data and understand the trends, based on which better recommendations can be made. Some of the major benefits of having an AI based algorithmic trading solution include faster reaction to changes, identification of patterns that are difficult to notice (by humans), and conducting analysis of data from multiple reference points or perspectives.

Client Risk Profile Analysis

All financial service providers have to manage risk profiles of their clients. Based on the evaluation of the risk profiles, appropriate services are suggested to the customers. However, manual management is time consuming and might be subjected to human errors. With AI integration, the risk profiles of clients can be categorized and products / services can be auto-classified based on the respective risk category. This will help to save considerable time and efforts.

Churn Prediction

Customer churn is one of the most common elements across all industries. While new customers are always appreciated, the actual revenue comes through recurring business which is generated through existing customers. Customer churn rate (attrition rate) needs to be minimized. However, should a business wait until it’s too late to retain its customers? AI based churn prediction models can provide indications of customers who wish to end their service agreements with a business. An early intimation can allow managers to make attempts to retain customers through alternative service arrangements, discount on renewals, or by providing other benefits.

Impact of AI in Fintech

AI in Fintech will allow financial institutions and organizations to streamline services and make them customer oriented with real time data processing. With AI driven chatbots, fintech companies will no longer have to use manual intervention to serve customers or address the concerns of users. All conversations can easily be handled with the help of smart chatbots which are becoming immensely popular these days. This also helps to increase customer satisfaction, reduce response time, and lower labour costs.

In the Fintech industry, data security is of paramount importance and ensuring privacy is really crucial. With the rise in technology disruptions, it has become a challenge to build adequate security parameters to protect data across all platforms. AI based security solutions can not only help to develop robust security systems, but also provide a smart system which learns from probes made by malicious programs that want to steal data. In a real world scenario, the AI component works similar to a human data security expert and provides the added advantage of eliminating emotional conflicts completely from the evaluation process.

AI in Fintech has also given rise to IPA (Intelligent Process Automation) which is the amalgamation of two distinct technologies, namely, RPA (Robotic Process Automation) and artificial intelligence. Augmenting AI to a rule-based robotic process automation system gives rise to another tool that not only automates tasks but also possesses decision-making capabilities. Intelligent process automation can analyse structured, semi-structured, and unstructured data, learn with time how to undertake different tasks, and take decisions on its own.

The implementation of new innovations in AI for Fintech might not be cost-effective today, but the long term implications are tremendous and will give rise to a new way of automating processes. In addition to simplifying complex processes, AI will help in increasing ROI to a great extent.

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Complaint Board
As On 30th April, 2020
Beginning of month Received during the month Resolved during the month Pending at the end of the month Reason for pendency
214 0 0 1 Closed by us, Filed ATR, SCORES approval awaited

Total clients served so far Total complaints received so far % complaint on customer base
70,000+ Customers 214 0.30%

Data is updated on 7th per month. It gets verified every quarter by independent auditor.

Data presented here is taken from company's inception

Importance given to satisfactory resolution as per prescribed TAT