Deloitte AI Institute

The Financial Services AI Dossier

Top uses for AI in the financial services industry — now and in the future

Using AI to improve customer experiences in finance

Aside from numerous FinTechs that are fully embracing AI, most firms in the financial services industry (FSI) are still in the very early stages of AI adoption and investment.

Although FSI leaders generally recognize and acknowledge the potential impact of AI on their businesses—and that AI is an inevitable part of the industry’s future, and the primary fuel for future growth and competitiveness—most AI investments and efforts to date have been limited to small-scale pilots and niche use cases focused on narrow parts of the business.

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of FSI execs say that new technologies will continue to drive global banking in the next five years, over regulation and changing customer behavior.

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of financial service providers already use AI for predictive analysis, voice recognition and others.

Facing the top obstacles

For most FSI firms, the important next step is to stop dabbling with AI and start embracing and industrializing it so that AI solutions can be deployed on a large scale across the entire enterprise. This would likely require core building blocks such as enterprise-wide data governance and clear strategies for harnessing the power of AI and data. Simply throwing more money at the problem won’t be enough.

  • The widespread uptake of disruptive technologies, taken together, is exacerbating the pressures facing the retail banking industry – demanding customers, rise of new competitors, and increasing levels of regulation, while market conditions have been significantly worsened by the COVID-19 crisis.
  • Retail customers today are less loyal and happy to shop around for a better quality of user experience and better value for money. They expect banks to look after their financial wellbeing and go beyond banking to support lifestyle needs, while ensuring data security and transparency.
  • The new entrants –fintechs, technology giants, or neobanks, are all consumer-oriented, technology-driven companies that are leveraging disruptive technologies to develop compelling propositions for their customers.

Three-quarters of FSI leaders cite four main barriers to AI adoption:

poor data, insufficient use cases, inadequate talent and skills, and lack of business buy-in

Where are the opportunities for FSI companies?

Scaling AI across financial organizations means addressing the challenges of data silos, internal departments, industry regulations, and data protection.

Improving the customer experience — not only for a firm’s end customers, but also for its internal customers such as agents, brokers, and financial advisors. For example, AI applications are helping make chatbots and IVR systems far more intelligent and sophisticated than before, improving the quality of automated customer interactions and seamlessly integrating and orchestrating multiple interaction channels. Similarly, predictive AI is being used to improve customer experience by engaging with customers more thoroughly and effectively throughout their entire lifecycle from personalizing marketing campaigns and promotions, to recommending individualized next best actions and plans

Automating and enhancing critical FSI processes such as fraud detection, payment processing, cash reconciliation, underwriting, and claims management. Some of these processes are highly repetitive and labor-intensive, making them prime candidates for automation. Others can greatly benefit from improved insights and have been using targeted analytics for decades; however, AI is lifting those analytics capabilities and insights to a whole new level.

Industry convergence is another key trend being driven by AI — and it’s not just limited to FinTechs. AI technologies, fueled by the explosion of digital data, are enabling entirely new products, services, and business models that blur traditional industry lines. And the speed, scale, and scope of this industry convergence seems to only be increasing.

Using AI and digital data to break down functional silos and generate insights that span the entire value chain. (For example, using data from an insurance chatbot to inform the underwriting process). However, capitalizing on these broad, large-scale AI use cases and opportunities would require the enterprise-level AI building blocks and industrialization capabilities which are still being developed.

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of people with a smartphone but without a bank account will use a mobile-accessible cryptocurrency account by 2025.

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of FSI execs plan to increase spending on AI infrastructure by greater than 10 percent in 2021, compared to 2020.

Understanding what can be achieved by AI today

Explore five use cases depicting how financial services-related businesses are harnessing the power of AI to revolutionize the way individuals and companies deal with money:

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Fighting fraud

Banking Fraud Analytics

Use AI and machine learning to detect transactional and account takeover fraud across the banking value chain.

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Chatbots that can do more than chat

Conversational AI

Use conversational AI solutions such as chatbots and virtual assistants to handle a wide range of consumer-facing activities — from helping consumers find a better credit card or cancel unneeded accounts, to negotiating collections.

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Hyper-personalization

360° Customer Experience

Use AI to acquire customers and deliver an ultra-personalized, end-to-end customer experience supported by deep AI-driven insights, including customer churn prediction/prevention, estimated customer lifetime value (CLV), marketing optimization, customer segmentation and personalization, and next best action.

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Underwriting that goes over and above

Insurance Underwriting

Use AI and machine learning to help enhance underwriting processes and risk evaluation, aid in decreasing decision times, and possibly improve the customer experience and bind rates.

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Trade operations made easy

Trade Operations Automation

Use AI and machine learning to help automate tasks such as trade reconciliation and operational exceptions remediation.

Navigating the future of AI in the FSI industry

The growing capabilities of artificial intelligence and the increasing amount of data available mean that financial firms need to implement AI strategies or risk being left behind by their competitors. Visionary firms must become “AI-first” businesses — leveraging machine learning technologies to create and deliver personalized products and services that meet the evolving needs of modern consumers.

Explore our five emerging AI use cases in the FSI industry to uncover future-driven opportunities:

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Payment with a smile

Biometric Digital Payments

Using facial recognition and other AI-based biometric technologies to process payments.

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Insurance that adapts to you

Usage-based insurance

Using AI to adjust insurance coverage and rates on-the-fly based on a customer’s actual behavior and needs.

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Stopping criminals in their tracks

Consumer Fraud Detection

Using AI to predict, prevent, and detect insurance fraud and questionable financial transactions.

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Making credit risk less risky

Credit Risk Analysis

Using AI to assess risk and creditworthiness for loans and credit cards.

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Not just location, location, location

Real Estate Price Estimation and Prediction

Using AI to estimate real estate values by analyzing a wide range of variables—including new types of data, such as geographic images from drones.

Get in touch

Monica O'Reilly Portrait

Monica O'Reilly

US Financial Services Industry Leader
US Risk & Financial Advisory Financial Services Industry Leader | Vice Chair

monoreilly@deloitte.com

Liliana Robu Portrait

Liliana Robu

US Consulting Financial Services Industry Leader | Principal

lrobu@deloitte.com

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