Artificial intelligence in finance is not new.  Impact of AI on financial services 2019 has almost taken over human intelligence. It is AI capabilities and business needs, heightening the interest over time in financial sectors. The impact of AI on financial services has created a storm in the financial industry with the three technologies’ combined power. These are machine learning, cognitive computing, and natural processing. The techniques and some other factors have motivated the financial sector to expand the use of artificial intelligence. These are the enormous growth of data, the intervention of new technologies like cloud computing, and increased customer expectations.

Artificial intelligence in the finance industry encompasses a broad range of organizations that deal with money. Some of the major sectors are banks, insurance companies, and stock brokerages. Artificial intelligence and machine learning in finance projects in these sectors have commendable performance.

Artificial intelligence in finance – Banking

With the application of artificial intelligence in finance, it is taking a rage in the industry. However, it is still in the early stage, and the usage percentage is only approx. 32%. AI technology areas which are mostly being used are

-Predictive analytics 

-Recommendation engines

-Voice recognition

-Responses.

It’s not only the financial start-ups but also some banking majors who have implemented artificial intelligence operations to streamline the banking operations. Some of the uses of artificial intelligence in the banking case study are as follows –

-JPMorgan Chase’s Contract Intelligence (COiN) platform uses image recognition software for analyzing legal documents. At the same time, it extracts important data points and clauses in seconds.

-Wells Fargo began a pilot project in April 2017 on an AI-driven chatbot that communicates with users to provide account information. This helps customers to reset their passwords using Facebook Messenger.

-In 2016 Bank of America reported a $3 billion innovation budget for AI.

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Benefits of AI in banking

AI is not limited to a particular technical area. Many implementations of it help to seamless operations in the financial sector.

-Chatbots help banks to serve customers more efficiently. However, such chatbots aren’t advanced enough for autonomous support but effective from a sales perspective. Powered by natural language processing, chatbots can help to handle agents’ calls with best-practice answers.

-Cognitive technologies  automate trades and personalize customer support by accessing user’s financial information, and their banking history. Also it aids financial advisory in some cases.

-Predictive analytics leverages a company on sales forecasting and advanced revenue prediction.

-Neural networks help agents to respond to common customer service questions using metadata sorting. Besides, it generates potential responses with a level of certainty attached.

-Handling unstructured data from several database storages of a financial firm is tedious to maintain. This often needs a good number of data analysts. However, with Artificial intelligence, now it has become much easier to handle with minimum human supervision.

-Credit-lending applications help credit decision-makers to utilize AI to achieve faster and more accurate risk assessment. It is machine intelligence that can analyze the capacity of applicants. Artificial Intelligence considers a wider variety of factors, which are the more informative and data-backed decision. For example, AI-based credit scoring uses more complex and sophisticated rules than those used in traditional credit scoring systems. Thus it becomes easy for the lenders to distinguish between the credit-worthy and high default risk applicants without an extensive credit history.

-Lenders can achieve nonlinear and dynamic credit risk models from AI derived from genomics and particle physics.

-AI works as instrumental in generating insights on outstanding debts that are hard for humans to spot.

-Identifying fraudulent transactions is a key area of AI applications. Algorithms like MasterCard’s Decision Intelligence technology analyses various data points in this respect. This is mainly applicable for preventing credit card fraud and money laundering.

Credit card fraudulent activity has grown exponentially in recent years. One of the prime reasons behind it is an increase in e-commerce and online transactions. AI boosted fraud detection systems analyze clients’ behavior, buying habits, and location to trigger a security alert when something contradictory occurs in the established spending pattern.

Similarly, banks implement an AI-based system to identify suspicious activities related to money laundering.

Robo-advisor is another invention of AI that analyses users' financial decision activities like spending, investing patterns. Based on this pattern, the financial firm can set advice relevant to the customers.

-Customer recommendations: Recommendation engines work as a key component for revenue growth. It works based on past data related to the users, bank offerings like credit and debit cards, investment plans, etc. Based on the recommendation engine analysis, banks can advise on the customers' best suitable investment options.

-Whether it is a sophisticated or most basic trading theory, all work on trading algorithms. These algorithms are based on the most basic AI reasoning.

Artificial intelligence in Insurance

Artificial intelligence in the insurance industry has brought revolutionary changes. NLP (Natural Language Processing) plays a critical role here, which helps implement the intelligent omni-channel user interface. This enables policyholders to initiate claims processes. These Omni-channels make it possible for customers to interact with insurance companies through various communication mediums like text, chat, voice, email, etc.

AI mainly drives the insurance industry in three ways from a savings perspective–for policyholders, brokers, and carriers.

Automated Policy Pricing: With the help of the Internet of Things (IoT) sensors, personalized data can be sent to pricing platforms. This, in other terms, drives usage-based insurance. So, if your lifestyle is healthy, you need to pay less premium for health insurance

Risk-based payment: Wearable sensor data can change trackless risky behavior directly related to less premium.

Faster settlement: IoT data enables carriers’ faster accessing to verified risk management information instead of relying on costly audits

Personalized coverage for customers: AI-based chatbots enable a seamless automated buying experience. The geographic and social data of the users' chatbots enables personalized interactions for the customers and on-demand insurance.

Automated recognition:  With advanced image recognition and personal identity verification, AI enhances the customers' buying experience.

Virtual claim settlement: Virtual claim adjustment through customized online interfaces make it faster and efficient to pay and settle accidental claims. Besides, it decreases the chances of fraudulent activity. Additionally, it enables P2P (peer-to-peer) insurance.

Predictive analytics in health insurance: The smart capability of AI-powered solutions is basically backed by predictive analytics, a part of Machine Learning. Health insurance companies encourage customers with no claim payment if they remain healthy.

Marketing of relevant products: Nowadays, insurance companies do not go by old blanket methods or cold calling methods to sell their products. Leveraging the power of AI, predictive analytics, and NLP, insurance companies can access the full profile and prospects of customers today. This enables them to analyze and predict customer preferences in a more mature way. As a result, they can refer to the exact products which the customers are opting for.

AI in Stock brokerage

In the earlier 2000s, Stockbrokerage was a traditionally human-based service that allowed them to buy and sell equities. However, this sector, as well as witnessed a shift today with software-based automation. Activities like executing trades, investors’ advisory services, discretionary trading, etc., which were manual a decade ago, are automated with software intervention.

AI in stock brokerage mainly operates in three ways –

AI for Trade Executions

Automated trading

Advisory Services

AI trading system and Automated trading – It depends on trade execution algorithms. As part of the process, when a trader executes a buy request, this buy order gets matched with the sell orders. This is essential for executing securities trades. Using statistical techniques, such trades are broken up into smaller orders. This, as a result, minimizes the impact on the stock prices once the trade is executed.

Since the algorithms can test trading systems based on past data, it performs validation more effectively. Consequently, this whole process helps to predict stock performance more accurately.  

Advisory Services –  Using machine learning techniques, the software determines the price patterns, performs sentimental analysis by exploiting blogs, news, analyst reports, social media feeds, etc. This provides not only accurate predictions on stock prices but also identifies future performance. Furthermore, identifying complex correlations between stock attributes is also possible using this technique.

Conclusion:

The applications mentioned above of artificial intelligence in finance are just a few examples. There are many more artificial intelligence applications in the finance industry, which are upcoming. These will capable of analyzing the brand sentiment at a more accurate level. Additionally, with the adoption of Blockchain and Cryptocurrency, we can expect more monetary safety in transactions. The use of artificial intelligence, no doubt, will make personal finance place more manageable along with the use of smart machines. Self-help virtual reality systems will turn customer care more sophisticated. There are lots of challenges associated with the proper implementation of AI in financial sectors. Furthermore, many still hesitate to bring it to reality. However, we can't restrict technological progress for the betterment of humanity.