2107 09051 AI in Finance: Challenges, Techniques and Opportunities

Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. AI-powered computers can analyze large, complex data sets faster and more efficiently than humans. Founded in 1993, The Motley Fool is a financial services company dedicated to making the world smarter, happier, and richer. The Motley Fool reaches millions of people every month through our premium investing solutions, free guidance and market analysis on Fool.com, top-rated podcasts, and non-profit The Motley Fool Foundation. The technology, which enables computers to be taught to analyze data, identify patterns, and predict outcomes, has evolved from aspirational to mainstream, opening a potential knowledge gap among some finance leaders. She’s super smart, works extremely long hours, picks up on patterns and trends, knows and uses all the latest tools, makes great predictions, is extremely accurate, and incorporates feedback and constructive criticism well.

Bank One implemented Darktace’s Antigena Email solution to stop impersonation and malware attacks, according to a case study. The bank saw a rapid decrease in email attacks and has since used additional Darktrace solutions across its business. A Vectra case study provides an overview of its work to help a prominent healthcare group prevent security attacks. ai in finance Vectra’s platform identified behavior resembling an attacker probing the footprint for weaknesses and disabled the attack. Having good credit makes it easier to access favorable financing options, land jobs and rent apartments. So many of life’s necessities hinge on credit history, which makes the approval process for loans and cards important.

AI in Finance: CFO Strategies for Successful AI Deployment

Today, companies are deploying AI-driven innovations to help them keep pace with constant change. According to the 2021 research report “Money and Machines,” by Savanta and Oracle, 85% of business leaders want help from artificial intelligence. When building AI-driven processes in finance, CFOs should consider how to design solutions with total transparency so that responsible humans can remain fully informed and accountable. Building processes to promote the strengths of people and machines, while avoiding their respective weaknesses, introduces a new collaboration that improves business performance and employee satisfaction. Successful finance teams design processes so that people and machines are each tasked with the actions they perform best.

The upskilling of policy makers will also allow them to expand their own use of AI in RegTech and SupTech, an important area of application of innovation in the official sector (see Chapter 5). Industry participants note a potential risk of fragmentation of the regulatory landscape with respect to AI at the national, international and sectoral level, and the need for more consistency to ensure that these techniques can function across borders (Bank of England and FCA, 2020[44]). Although many countries have dedicated AI strategies (OECD, 2019[52]), a very small number of jurisdictions have current requirements that are specifically targeting AI-based algorithms and models.

The future of Artificial Intelligence in finance

One way it uses AI is through a compliance hub that uses C3 AI to help capital markets firms fight financial crime. Announced in 2021, the machine learning-based platform aggregates and analyzes client data across disparate systems to enhance AML and KYC processes. FIS also hosts FIS Credit Intelligence, a credit analysis solution that uses C3 AI and machine learning technology to capture and digitize financials as well as delivers near-real-time compliance data and deal-specific characteristics. Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization.

Nowadays, consumers expect response times to be faster and more convenient to them, no more office hours — 24/7 communication is the new normal for many. However, for many businesses, it’s almost impossible to ensure round-the-clock communications, and this is where conversation AI is coming in. For a preview, look to the finance industry which has been incorporating data and algorithms for a long time, and which is always a canary in the coal mine for new technology. The experience of finance suggests that AI will transform some industries (sometimes very quickly) and that it will especially benefit larger players. The advent of ERP systems allowed companies to centralize and standardize their financial functions. Early automation was rule-based, meaning as a transaction occurred or input was entered, it could be subject to a series of rules for handling.

Examples of AI in Finance

What is more, many banks’ data reserves are fragmented across multiple silos (separate business and technology teams), and analytics efforts are focused narrowly on stand-alone use cases. Without a centralized data backbone, it is practically impossible to analyze the relevant data and generate an intelligent recommendation or offer at the right moment. If data constitute the bank’s fundamental raw material, the data must be governed and made available securely in a manner that enables analysis of data from internal and external sources at scale for millions of customers, in (near) real time, at the “point of decision” across the organization. Lastly, for various analytics and advanced-AI models to scale, organizations need a robust set of tools and standardized processes to build, test, deploy, and monitor models, in a repeatable and “industrial” way. To establish a robust AI-powered decision layer, banks will need to shift from attempting to develop specific use cases and point solutions to an enterprise-wide road map for deploying advanced-analytics (AA)/machine-learning (ML) models across entire business domains. To enable at-scale development of decision models, banks need to make the development process repeatable and thus capable of delivering solutions effectively and on-time.

• Here are a few examples of companies providing AI-based cybersecurity solutions for major financial institutions.
• As per Autonomous Next research by Business Insider Intelligence, the aggregate potential cost savings for banks by using AI applications is estimated to be about $447 billion by 2023, with the front and middle office accounting for $416 billion of that total.
• The widespread adoption of AI and ML by the financial industry may give rise to some employment challenges and needs to upgrade skills, both for market participants and for policy makers alike.
• Furthermore, larger enterprises tend to use combinations of different software tools and platforms to house their data.