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The Growing Impact of AI In Fintech

Artificial intelligence has taken the world of tech by a gust. AI in the fintech market is being used at an increasing rate and is poised to have a major impact on the industry to automate a variety of their internal processes, as well as boost their bottom line. Forbes, in its report, states “AI will save the banking industry more than $1 trillion by 2030”.

In the United States, about 2.5 million financial employees are exposed to AI technologies. The potential cost of $490 billion in the front office (for Distribution), $350 billion in the middle office, and $200 billion in back office (for Manufacture), totaling $1 trillion across fintech.

Use cases of AI in Fintech

In the Front Office

The applications focus on integrating financial data and encounter software agents that can hold conversations with clients, as well as support staff.

In the Middle Office

AI oversights, risk-management, and valuable KYC systems.

In the Back Office

AI is used to determine credit risk with new types of data, takes credit underwriting risk and assess claims damage using machine vision, and select investments based on alternative data combined with human judgment.

Let's take a quick look at some areas where AI has bolstered Fintechs to make their services faultless and unified.

AI for Customer Service

It’s important to note that AI for customer service does not have a “behind-the-screen” role in the world of business. It plays an important role. Yes, we are talking about chatbots!

Chatbots can deliver customer service or expert advice at a low cost saving time and money. These chatbots, enables financial institutions to undertake numerous internal and external communications. Here, Natural Language Processing (NLP) plays a vital role along with deep machine learning algorithms to understand language and generate responses more naturally. Moreover, chatbots can be programmed as financial advisors, expense saving bot, banking bot and tax bot thus increasing convenience for both customers and businesses by reducing labour cost as well as human errors.

95% of financial firms state that they plan to increase the use of chatbots in the upcoming years, with some estimating, chatbots will handle 80% of all customer interactions by 2020.

Human interactions are undoubtedly more complex compared to chatbots and customers engaging with chatbots can translate into significant savings of cost and time.

In October 2016, both Bank of America and MasterCard introduced their chatbots, Erica and Kai, respectively. These allowed customers to ask questions about their accounts, initiate transactions and receive advice via Facebook Messenger of Amazon’s Echo tower. Michelle Moore, the head of digital banking at Bank of America declared, “What will banking be in two, three or four years? It’s going to be this.”

HSBC has created its AI-powered chatbot named “Amy” and The American Express has a bot named “Amex bot”, these bots help customers with questions about their accounts, personal information, and cuts-off front office and helpline staffing costs.

Source: Bank Of America

AI in Risk Management

Risk management is an integral part of financial companies. It aims to control the process of taking risks by making potential losses more predictable. Artificial intelligence has made its stand when it comes to fraud detection and security as it provides tools and AI solutions. One of the traditional methods of fraud detection includes computers analyzing structured data against a predefined set of rules

With advanced deep learning algorithms, new features can be added to the system for dynamic adjustments. According to Samir Hans, an advisory principal at Deloitte, “With cognitive analytics, fraud detection models can become more robust and accurate. If a system kicks out something that it discovers as potential fraud and a human determines it’s not fraud because of X, Y, and Z, and next time it won’t send a similar detection. The computer is getting smarter and smarter”.

The Success story of PayPal with Artificial Intelligence

PayPal, processed about $ 235 billion in 2015 by 4 million transactions from its 170 million customers. PayPal used simple and linear models in the past But, Today, its algorithms can mine data from a customer’s purchase history to review patterns that are likely fraud. A linear model can only spot 20-30 variables, while deep-learning technology can command about 1000’s of data points. These capabilities helped PayPal to distinguish innocent transactions from doubted ones.

Hui Wang, Senior Director of Global Risk Sciences, PayPal states “ What we enjoy from advanced Machine Learning is its ability to consume more data and handles layers of abstraction and be able to see things that even humans might not see”.

Source: PayPal's fraud management filters

AI in Insurance underwriting and claims

AI helps in accelerated underwriting and fast-tracking claims. AI can help in underwriting by updating the analysis as and when new data becomes available and optimize risk management insights to generate more accurate recommendations.

A PWC report predicts that AI will automate underwriting by 2020, especially in markets where data is available. To be precise, AI can help automate large volumes of underwriting. It is also predicted that advanced AI will enable personalized underwriting, taking into account unique behaviors. However, AI cannot completely replace underwriters but can alter their responsibilities such as assessing and pricing risks, providing more risk management and product development feedback.

The insurance industry also uses AI technology for looking at thousands of claims, customer queries and large amounts of diverse data. AI is often seen as an innovative effort in the insurance sector from customer service to claims processing.

Insurance claims are formal requests for payment to the insurance companies. Insurance companies then review the claim for validity and payout once approved. The claim process is fairly manual. Insurance agents manually log customer information and incident details. According to an Experian report, data quality suffers 55% of data errors, while typos comprise 32%. AI can improve accuracy by reducing manual input with cognitive models. AI also can analyze unstructured data like handwritten forms and certificates

Artificial intelligence in trading

There is a huge transition from human-constructed models to true AI. For decades, financial investment companies have relied on computers to make trades. But these computer models can only utilize historical data, that are often static, requires human intervention, and do not perform as well when it comes to dynamic market changes.

AI utilizes advanced techniques like deep learning, a form of machine learning called Bayesian networks, and evolutionary computation which is inspired by genetics. AI trading software can absorb gigantic volumes of data to make predictions about the financial market. To understand global trends, they can consume everything from books, internet, tweets, news reports, financial data, and even statistical data from international monetary policy. “If we all die, it would keep trading.”, says Ben Goertzel, co-founder, SingularityNET.

According to the Eurekahedge report, AI trading trends in Eurekahedge are shown below.

Source: Eurekahedge

Robo - Advisory

Robo-advisors are digital platforms that provide algorithm-driven financial services with minimal human supervision. Robo-advisors allow customers direct access to the service. Unlike humans, Robo-advisors monitor the market trends non-stop and are available 24/7. They can help with more repetitive tasks such as account opening and money transferring.

The process involves clients, answering to simple questionnaires about risk appetite or liquidity factors, which Robo-advisors then translate into investment logic.

Source: Accenture Research

Parting Thoughts

  • The significance of AI in fintech is becoming more apparent by the day. As banks and other financial institutions strive to beef up security, streamline processes, and improve financial analysis, AI is becoming the technology of choice.
  • AI in fintech is bound to remake this finance industry more digitized than ever before. Its final goal would be to create “frictionless” banks: no branches, no managers, no credit cards, no fraud, no menial reporting activities.

Yes! It would certainly be a great world to live in. Can’t wait for it!!

Artificial Intelligence services and solutions from doodleblue

doodleblue offers Industry-leading AI services to our clients by developing applications that are specially crafted to fulfill their requirements and improve their ROI. We deliver advanced insights, power business alerts, and drive intelligent recommendations to users that help businesses make better decisions.

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