An overview of AI in finance

Artificial intelligence (AI) refers to tech that can perceive, learn, and problem-solve in a way similar to humans.

AI in finance is the use of intelligent technology with the goal of improving the speed, efficiency, and accuracy of the work done by humans in the financial services industry. This includes data analysis, forecasting, fraud detection, and customer service.

Knowledge, as the saying goes, is power. And today, it arrives in the form of data.

But what if there’s so much of it that a human could never have enough time to draw meaningful conclusions from it?

This is where AI comes in. With the use of automated machine learning algorithms and predictive AI models, patterns and correlations about market trends or customer sentiment can emerge from “the noise.”

Businesses will have actionable insights in real-time to make informed decisions, be able to increase operational efficiency, and have predictive analytics for better forecasts to mitigate risk. Any of those could be an edge over competitors.

Examples of AI in finance

Here’s where AI is transforming financial operations (finops):

• AI models for forecasting and predictive analysis: That’s why businesses use AI models to run scenario analysis to identify vulnerabilities, establish contingencies, and mitigate potential impact.

• Blockchain: Blockchains are shared, decentralized, digital ledger systems. Because they are essentially massive databases, some organizations utilize AI to analyze them to identify trends.

• Credit decisions: In addition to credit history, algorithms can also factor in data like social media activity to more accurately assess a person’s creditworthiness.

• Customer support: Letting chatbots take on the FAQs and usual tasks lessens the burden on human customer service agents, giving them bandwidth to handle more complex cases.

• Fraud detection: AI models are playing an increasingly crucial role in enhancing cybersecurity. It analyzes and trains on large amounts of data to indicate and predict anomalies that indicate threats.

• Invoice management: AI can easily take on the tedious task of receiving and sending invoices, even flagging those that may be fraudulent.

• Quantitative trading: Investors are using AI to create algorithms to identify trends, analyze historical data, and then make trades faster than they can.

• RegTech: Regulatory technology aims to help the financial services industry take on the complex and data-heavy task of financial reporting. Doing so with the automation of AI enables them to meet regulatory compliance more efficiently.

• Risk management: By processing data faster from more sources, AI can provide to finops insightful forecasts that can inform comprehensive risk management decisions.

• RPA/account reconciliation automation: Reconciliation involves comparing internal financial records with external statements, like from a bank, to help ensure accuracy. This time-consuming process can be automated with AI.

Five ways artificial intelligence can benefit the financial services industry

One insurance company launched a generative AI copilot for actuaries that reduced average modeling completion times by 90%.

With stats like that, it may seem like AI is on course to replace humans in the financial services. But by allowing AI to take on menial and manual tasks like data entry, we think it will allow humans to concentrate their time and energy toward tasks AI can’t do as well: critical thinking, strategy, and innovation.

Here's where AI in finance is doing just that:

• Enhanced decision-making and scenario analysis for financial planning and analysis: AI tools can transform vast volumes of data into actionable insights to inform decision makers. AI models can also be used to predict how their organizations perform in certain scenarios, enabling them to plan accordingly for whatever’s on the horizon.

• Increased operational efficiency: The accuracy, speed, and automation AI tools bring to finops reduces errors and boosts profitability.

• Enhanced customer experiences and personalization: AI-chatbots use machine learning and algorithms to analyze user data and preferences to provide personalized customer service experiences.

• Streamlined financial reporting cycles: A recent survey of finance leaders by SAP and Oxford found that 57% of respondents state financial closing as the most time-consuming process. AI tools can be used to analyze datasets to quickly identify outliers or risks, leading to a more unified financial reporting process.

• Increased employee productivity and innovation: Allowing AI tools to take over data-intensive tasks allows organizations to focus their human talent on problems AI can’t do as well: critical and strategic thinking. After all, AI tools can provide insights, but humans make the decisions.

• Reduced costs: The increase in accuracy and speed of AI will help human employees save time, enabling them to innovate and be more creative.

• Optimization of capital allocation and investment decisions: The same AI models used to run scenario analysis can also inform how to best invest capital.

• Compliance and regulatory reporting: There are machine learning models that can help organizations stay-up to date with all matters regulatory compliance, financial reporting, and risk management.

AI in finance can automate tasks like data entry with greater speed and accuracy than humans. It can process vast volumes of data with ease to identify discrepancies, offer insights, and run predictive analytics.

Increased operational efficiency is the goal. However, we think it’s the combination of AI-assistance and human critical thinking and intuition that will prove the most significant driver of growth in the financial services industry.

Challenges and ethical considerations of AI in finance

It’s exciting to consider the exponential potential AI will bring to the financial services industry. However, it’s vital to keep in mind the challenges and ethical concerns that will arise with its emergence.

In its ideal state, AI in finance will be used in ways that respect fairness, transparency, privacy, security, and society at large. But how is something like fairness defined? Some have raised eyebrows at an AI model factoring in a person’s social media activity to determine their creditworthiness. Is that fair? And by doing so, did the AI violate that person’s privacy?

AI can be used to draw actionable insights from data to help inform decision makers. Can those insights be used to reinforce bias towards a person or group? We speak of regulatory compliance with laws like the Dodd Frank act in the United States, but what of the regulations around ethical use of AI?

These are all crucial questions that must be reckoned with as AI becomes more intertwined with the financial services industry. Answering them will be the objective of an organization’s AI ethics steering committee that will be composed of developers, policymakers, business leaders, civil society organizations, academic institutions and end users. The more diverse the stakeholders are, the more perspectives that can be incorporated into the policy.

Ongoing oversight with “a human in the loop” will enable policies to refine and adapt over time and as technology and society progresses.

Comprehensive training, in the form of curriculums, training modules, and feedback mechanisms, will also be necessary to integrate the policies throughout the organization.

The future of AI in finance

Generative AI to kickstart financial reporting. Predictive analytics to inform decisions. Even blockchains, with the traceability and transparency they provide, are being used to help meet regulatory compliance. AI tools are becoming more integrated with the financial services industry with each passing day.

It would not be a stretch to imagine these tools becoming faster and more accurate as computers enhance and machine learning matures.

Lack of accuracy, however, is not the chief concern among users. Rather, it’s mistrust that persists among users toward algorithms and AI models and the lack of understanding of how they form conclusions on, for example, creditworthiness.

The emerging field of explainable artificial intelligence seeks to produce AI models that make its inner workings transparent to human users. Doing so allows decision makers to clearly see the rationale behind the conclusions given and judge them accordingly when factoring in their own expertise.

Again, we maintain it’s the combination of AI data processing and human critical thinking will result in better decision making.

Which leading companies are currently using AI?

Before we get too far ahead into tomorrow, here are some companies already using AI in finance today:

Mercedes-Benz Mobility

Through Mercedes-Benz Mobility, private and commercial customers can finance or lease vehicles though flexible rental and subscription models. Despite already implementing an automated payment system, accounting teams still had to manually match invoices when there was missing or incorrect info, costing them precious hours of their work week.

To improve this, they consulted SAP Services and Support to add a “self-learning” functionality to their SAP Cash Application software. This allowed it to evaluate the information available to allocate payments automatically in the event of incorrect details. Thanks to AI and machine learning, 58% of unallocated invoices were automatically and successfully processed, saving an average of 5-10 minutes per invoice. That’s 5-10 minutes, multiplied by the thousands of payments processed per day.

Mitsui

Mitsui, one of the largest general trading companies in Japan, selected SAP to support their company-wide “Integrated Digital Transformation Strategy.”

One pain point they were looking to solve was the reconciliation and clearing of unprocessed bank statement information. By employing AI and machine learning techniques, they were able to automate this process, saving employees 36,000 hours a year with an accuracy of over 90%.

The company also began employing chatbots in their domestic core system to reduce the burden on their maintenance staff and users.

How to get started with AI in finance

Start by implementing a cloud-based ERP system. ERP, or enterprise resource planning, is a software system that’s designed to help finops run more efficiently. All the core business processes, like HR, manufacturing, supply chain, and services, can be managed in an integrated system.

Finance is perhaps the most important because it’s the most concerned with money. It manages the ledger, tracks accounts payable and receivable, generates financial reporting, and more.

Today’s ERP systems are taking advantage of AI in finance to drive growth and innovation. By delivering actionable insights in real-time, reducing cost of operations, and mitigating risk, AI seeks to give organizations a newfound competitive edge.

The AI tools that can aid with regulatory compliance and risk management are embedded into an ERP like SAP S/4HANA but enterprise AI may take the form of generative AI copilots or adaptive learning systems in the workplace.

Pilot programs that allow for gradual integration into workflows can also help employees acclimate. Transparent discussions about AI and development of an organization AI ethics policy may also help alleviate concerns about being replaced.

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