AI in Finance: 10 Use Cases You Should Know About in 2023 The AI-powered spend management suite

In short, it means that companies will likely invest heavily in unlocking and understanding the data they have and seek to acquire more to make smart business decisions. However, it’s not just the quantity of data that matters, it’s the quality of the analysis that counts. Investments in consumer behavioral analysis are set to rise, and there is a renewed focus on gaining a deeper understanding of the current market. For many IT departments, ERP systems have often meant large, costly, and time-consuming deployments that might require significant hardware or infrastructure investments.

  • A few months ago, Maybe was rebuilt from the ground up, this time with GPT, the technology behind ChatGPT, as the foundation of the platform.
  • Currently, most smart contracts used in a material way do not have ties to AI techniques.
  • AI use-cases in finance have potential to deliver significant benefits to financial consumers and market participants, by improving the quality of services offered and producing efficiencies to financial firms, reducing friction and transaction costs.
  • Among executives whose companies have adopted AI, many envision it transforming not only businesses, but also entire industries in the next five years.

Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates or related entities. Deloitte Insights and our research centers deliver proprietary research designed to help organizations turn their aspirations into action. We observed a similar pattern in terms of the skills gap identified by different segments in meeting the needs of AI projects (figure 12). While a higher number of implementations undertaken could partly explain this divergence, the learning curve of frontrunners could give them a more pragmatic understanding of the skills required for implementing AI projects. However, the survey found that frontrunners (and even followers, to some extent) were acquiring or developing AI in multiple ways (figure 9)—what we refer to as the portfolio approach.

One of the main problems we face in implementing AI is getting people acquainted with the idea and getting them on board with the fact that intelligent machines would replace human intelligence. Many professors at MIT and the people at Boston Consulting Group are convinced that AI will only help the company to achieve sustainable profit. They can clearly understand behind-the-scenes operations that frees up the accountants to engage more with strategic decision-making procedures. Bring your expenses, supplier invoices, and corporate card payments into one fully integrated platform, powered by AI technology. In fact, right before the pandemic, a study by Juniper Research was predicting that AI-powered chatbots will be saving financial institutions over $7 billion annually by 2023.

solve real challenges in financial services

This, in turn, maximizes the accuracy and efficiency of audits and makes it easy to audit 100 percent of a firm’s financial transactions, not just mere samples. The impact of AI in the accounting and finance industry is phenomenal, and it is also innovating how they operate and build products and services. Recent AI advancements are rapidly changing the face of accounting and finance in many ways. Josh Pigford, the founder and chief executive of Maybe, had been building a personal finance management platform that could help people make financial decisions when ChatGPT debuted. A few months ago, Maybe was rebuilt from the ground up, this time with GPT, the technology behind ChatGPT, as the foundation of the platform. These are deep waters where sharks are swimming and potent forces emerge to significantly change, if not redefine, banking as we know it today.

Thus, cost saving is definitely a core opportunity for companies setting expectations and measuring results for AI initiatives. Many companies have already started implementing intelligent solutions such as advanced analytics, process automation, robo advisors, and self-learning programs. But a lot more is yet to come as technologies evolve, democratize, and are put to innovative uses. In cases of credit decisions, this also includes information on factors, including personal data that have influenced the applicant’s credit scoring.

Fraud detection is one of the key areas where AI can provide significant support to finance departments. Artificial intelligence can be used to analyze large datasets and identify fraudulent activities – such as credit card fraud or money laundering – in real-time. This AI-based way of processing invoices is much more efficient and less prone to error than the traditional one, where human intervention is needed at almost ever step.

  • Kavout uses machine learning and quantitative analysis to process huge sets of unstructured data and identify real-time patterns in financial markets.
  • For developing an organizationwide AI strategy, firms should keep in mind that these might be applied across business functions.
  • Consumers are hungry for financial independence, and providing the ability to manage one’s financial health is the driving force behind adoption of AI in personal finance.
  • Deep learning neural networks are modelling the way neurons interact in the brain with many (‘deep’) layers of simulated interconnectedness (OECD, 2021[2]).
  • Additionally, 41 percent said they wanted more personalized banking experiences and information.

Vectra’s platform identified behavior resembling an attacker probing the footprint for weaknesses and disabled the attack. Vectra offers an AI-powered cyber-threat detection platform, which automates threat detection, reveals hidden attackers specifically targeting financial institutions, accelerates investigations after incidents and even identifies compromised information. Time is money in the finance world, but risk can be deadly if not given the proper attention.

How is AI technology used in finance?

The data advantage of BigTech could in theory allow them to build monopolistic positions, both in relation to client acquisition (for example through effective price discrimination) and through the introduction of high barriers to entry for smaller players. The difficulty in comprehending, following or replicating the decision-making process, referred to as lack of explainability, raises important challenges in lending, while making it harder to detect inappropriate use of data or the use of unsuitable data by the model. Such lack of transparency is particularly pertinent in lending decisions, as lenders are accountable for their decisions and must be able to explain the basis for denials of credit extension. The lack of explainability also means that lenders have limited ability to explain how a credit decision has been made, while consumers have little chance to understand what steps they should take to improve their credit rating or seek redress for potential discrimination. Importantly, the lack of explainability makes discrimination in credit allocation even harder to find (Brookings, 2020[20]).

This leaves our financial team with more time focused on the future instead of just reporting the past. High volume, mundane processes, such as invoice entry, can lead to fatigue, burnout, and error in humans. The end result is better data to work with and more time for the finance team to focus on putting that data to use. Specific software, such as enterprise resource planning (ERP,) is used by organizations to help them manage their accounting, procurement processes, projects, and more throughout the enterprise. Examples of back-office operations and functions managed by ERP include financials, procurement, accounting, supply chain management, risk management, analytics, and enterprise performance management (EPM). Assess existing talent, identify skill gaps, provide training opportunities, and recruit individuals who are equipped to handle future use cases as they emerge.

Fintech: Future of AI in Financial Services

KPMG’s multi-disciplinary approach and deep, practical industry knowledge help clients meet challenges and respond to opportunities. While these skills are often necessary in the initial stages of the AI journey, starters and followers should take note of the skill shortages identified by frontrunners, which could help them prepare for expanding their own initiatives. Frontrunners surveyed highlighted a shortage of specialized skill sets required for building and rolling out AI implementations—namely, software developers and user experience designers (figure 13).

AI leaders in financial services

Based on this output and an assessment of the information submitted by the customer, the credit analyst determines that the requested line of credit is acceptable and grants approval. If the tool had identified any red flags, the credit analyst would have needed to validate the information before incorporating it into the final credit decision. But, the adoption of generative AI in finance functions entails challenges, including accuracy and data security and privacy.

Generative AI will eventually collaborate with traditional AI forecasting tools to create reports, explain variances, and provide recommendations, thereby elevating the finance function’s ability to generate forward-looking insights. The enhancements will empower finance professionals to make more informed strategic decisions, leading to improved operational efficiency and effectiveness. Technology disruption and consumer shifts are laying the basis for a new S-curve for banking business models, and the COVID-19 pandemic has capital campaigns accelerated these trends. 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.

Why CFOs should have artificial intelligence on their minds

Policy makers and regulators have a role in ensuring that the use of AI in finance is consistent with promoting financial stability, protecting financial consumers, and promoting market integrity and competition. Emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI without stifling innovation. Existing regulatory and supervisory requirements may need to be clarified and sometimes adjusted to address some of the perceived incompatibilities of existing arrangements with AI applications. AI in finance should be seen as a technology that augments human capabilities instead of replacing them. At the current stage of maturity of AI solutions, and to ensure that vulnerabilities and risks arising from the use of AI-driven techniques are minimised, some level of human supervision of AI-techniques is still necessary.

IT teams will play a pivotal role in prioritizing generative AI investments and addressing data security concerns surrounding the use of AI in finance function applications. Initiate adoption with use cases whose barriers to entry are low, such as investor relations and contract drafting. Finance personnel will likely find that applying the new technology in real use cases is the best way to climb the learning curve.

By working with supplier-specific models, Yokoy’s AI-engine is able to process invoices with much higher accuracy rates than other invoice automation apps on the market. With the help of artificial intelligence, this process can be almost fully automated, saving time, reducing costs, and providing valuable insights into spending patterns, for increased spend control and better forecasts. While this number may seem unrealistically high, the same study found that AI technologies are already used by 52% of finance leaders, in one way or another. More than half of the surveyed leaders reported that they’ve already integrated some form of AI technology into their daily work.