The application of AI has increased rapidly across industries to streamline operations, make more informed decisions, automate processes, and improve accuracy. Although the Financial Services industry is among the leaders in the adoption of AI, it is still a long way from fully tapping the benefits of AI. The adoption of AI in the Financial Services industry began in 2008 in its true sense with the most impact on a few segments of the industry including, asset management, lending, insurance, and investment banking.

This paper discusses how AI has benefitted the Financial Services industry and the potential of AI going forward.

AI is automating processes and improving decision making across the Financial Services space

Financial services companies use AI to make data-backed decisions, manage default risk, prevent fraud, conduct algorithmic trading and provide more personalized products and services. Various uses of AI is expected to help the financial services companies reduce operational and business expenses by streamlining operations, generating revenue through new revenue streams and make financial services more accessible to people. AI is impacting every segment of the Financial Services industry with the greatest impact on the three largest segments of the industry – asset management, insurance, and investment banking.

Insurance companies are using AI to improve policy selection and service quality

SoftBank invested $300 million in the Series D funding of Lemonade, bringing the total funding of Lemonade at $480 million as on 11th April 2019.

The insurance sector is dominated by legacy companies and product lines that have not evolved for decades. AI has led to the emergence of several insurtechs such as Lemonade, that use AI-based tools to provide insurance services. Lemonade is a licensed insurance carrier that uses AI-powered bots to digitize insurance buying experience for renters and homeowners making it easy for anyone to purchase insurance with the help of a smartphone. Major investors like the SoftBank Group have seen how AI and big data has helped Lemonade to reach more than half a million homes in less than two years. SoftBank invested $300 million in the Series D funding of Lemonade, bringing the total funding of Lemonade at $480 million as on 11th April 2019[1].

The new players are gradually chipping the market share of legacy business. According to a CFTE survey, 98% of insurance executives expect that AI will be critical in the industry[2]. Although several industry players including, carriers, program administrators, managing general agents, and managing general underwriters have experienced the benefit of AI, it is yet to be used in some of the key areas of the insurance sector.

The key areas where AI is being applied within the insurance sector include customer service, underwriting, claim management, policy management, and purchase management. In policy management, insurance

companies are using AI to make personalized policies based on data collected from multiple sources. In customer service and claim management, AI helps insurance companies to report a notice of loss, automate damage evaluation and anticipate patterns in claim volume. For example, major insurance carriers such as State Farm and Allstate use AI to track automobile insurance clients and detect unsafe driving.

Asset managers are making decisions less biased by combining AI and alternative data

AI-based sentiment analysis tool can help asset management companies to analyze clients’ social media data, financial data, and other client information to take unbiased decision based on data gathered from multiple sources.

AI when applied to financial data, news, industrial sensor data and social media data can help asset management companies to analyze data from different data points and make investment decisions. AI-based sentiment analysis tool can help asset management companies to analyze clients’ social media data, financial data, and other client information to take unbiased decision based on data gathered from multiple sources. Although, asset managers have begun experimenting with AI to optimize their operating models in transaction management and risk management, the greatest impact of AI is expected to be on data analysis of asset management companies.

Traditional asset management companies have started to use AI-based operating models to evaluate portfolios. BlackRock’s Aladdin Risk Platform is an ML-based operating system that provides investment managers with risk analytics and portfolio management software tools. The platform helps individual investors and asset managers to assess the level of risk or return in a portfolio of investments. Aladdin can be used for various functions including portfolio management, trade execution, analysis and risk management, and investment operations. BlackRock claims that Aladdin can automatically monitor more than 2,000 risk factors (for example interest rates and currency rates) per day and test portfolio performance under different economic conditions.

Investment banks are using AI to automate workflow and make decisions using algorithms

AI in investment banking is an important tool when combined with the right data to provide better customer services, automate processes and improve efficiency. Investment banks are investing in AI to carry out activities, in seconds, that would otherwise take days to complete. Reduced time and cost involved in the execution of a deal is expected to allow investment banks to exponentially increase the number of deals they do. AI is expected to impact the operations of investment banking from deal sourcing and risk modeling to automating execution in equity and debt capital markets across various functions of middle and back office. Investment banks can strengthen opportunities in areas such as M&A by implementing AI in investment decisions and due diligence.

Although AI is expected to create opportunities for investment banks, it is expected to challenge the traditional investment banks with increased competition. Several fintechs have started to use AI-based algorithms to provide M&A services, and companies have started to conduct bilateral transactions without the involvement of investment banks. For example, EquityUp a fintech company uses a platform based technology to streamline investor onboarding and administration for lower to middle-market M&A automation. EquityUp uses an automated AI-based model that uses critical data points to recommend on valuation, deal structure, and investors which are the best match for the deal. The use of AI-based recommendation model has helped EquityUp to improve deal outcomes, simplify fundraising and streamline transactions.

AI-focused M&A deals have started to accelerate in the Financial Services industry

Financial service companies have started to analyze transactions and optimize operations with AI-based tools. The use of AI in financial services companies is expected to impact in three major ways – cost savings, change in investment patterns, and mergers and acquisitions.

The use of algorithm and technology is expected to help financial service companies to save around $1 trillion or 22% of their costs by 2030 due to a reduction in operating expenses led by AI[1]. This will happen as AI replaces employees in the front office, back office, and middle office, and it increases the efficiency of financial services companies by streamlining operations and automating processes. Through optimization of operations AI will also help financial service companies to reduce their outsourcing cost and streamline operations.

The various benefits of AI have led to an increase in investments in AI-based tools. According to a study conducted by PricewaterhouseCoopers, 52% financial service executives say that the companies in which they work are investing in AI. AI and ML investments of financial service companies increased from $1.7 billion in 2013 to $15.2 billion in 2017. 

According to a report by CB Insights, 18 fintech startups were acquired by 10 of the top 50 US banks including Credit Suisse, BBVA, TD Bank, BNP Paribas and Goldman Sachs between 2013 and 2018.

AI has not only affected the operations of the traditional financial service companies but has also led to an increase in the number fintechs and technology startups. This has led to an increase in the acquisition of companies that develop AI-based technologies and support financial services companies. The Financial Services industry has witnessed an increase in M&A deals, VC investments and CVC investments in early-stage AI companies. According to a report by CB Insights, 18 fintech startups were acquired by 10 of the top 50 US banks including Credit Suisse, BBVA, TD Bank, BNP Paribas and Goldman Sachs between 2013 and 2018.

Work has started towards AI in financial services but a lot more is yet to happen

AI is in its early stages of adoption in the Financial Services industry. According to Forbes, 65% of senior financial management executives expect positive changes from the use of AI in the Financial Services industry. This indicates that only one-third of the financial services companies have taken steps to implement AI into their processes as of 21st February 20197.

Although the use cases of AI in the Financial Service industry is limited to certain steps of the workflow, its potential benefits is expected to bring technological progress in the industry. Many traditional financial service companies such as Mizuho Financial Group have realized the benefits of AI. Mizuho is working on AI-based market forecasting tool for its asset-liability management and treasury portfolio division. The tool will use time series prediction logics to extract similar trend dates and create future market trend predictions. This will help Mizuho to find early market signals of market volatility and forecast the market using advance AI technologies.

The AI solutions currently in testing is expected to accelerate the democratization of financial services and make high-quality financial services easily accessible for everyone. AI is also expected to help financial service companies to reach out to customers across different geographies and demographics. AI is thus expected to make financial services more accessible creating a stable financial environment.

References

Disclaimer:

This publication contains general information only and is based on the experiences and research of Anplify professionals.  Anplify is not, by means of this publication, rendering business, financial, investment, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Investments are subject to risk, including the loss of principal. Because investment return and principal value fluctuate, shares may be worth more or less than their original value. Some investments are not suitable for all investors, and there is no guarantee that any investing goal will be met. Past performance is no guarantee of future results. Talk to your financial advisor before making any investing decisions.