Use of AI in Finance

Use Of AI In FP&A – A Definitive Guid

AI has taken over; have you jumped on the bandwagon yet, or are you waiting for the “trend” to burn itself out?

If you fall into the latter category, you might be waiting forever and losing lucrative opportunities while your competitors capitalize on AI.

AI is now vastly being used in banking, insurance, investing, financial modelling, tailored financial processes, and also for providing business insight within a few clicks.

The numbers don’t lie, specifically for finance –

• The global market for AI is set to grow at 30% between 2022-2032 and is predicted to be a $130 billion industry by 2027.
• As per a survey by the World Economic Forum, where 151 financial organizations in 33 countries reported that –

1. 85% of participants were already using AI in some capacity.
2. 77% of these believed that AI would be critical to their businesses.

https://marketresearch.biz/report/generative-ai-in-finance-market/

If you are still on the fence about how does AI really help in finance functions – let us explain.

Practical Implications of AI: Use Cases

At its core, finance is about analysing numbers to conduct risk assessments and predict returns. AI helps us with the financial automation of repetitive tasks, but it isn’t limited to this. From spotting complex patterns and sifting through data and documents to conducting real-time analytics, the digital transformation has paved the way for more accurate and faster forecasting, improved budget management, and overall, better financial reporting.

https://www.statista.com/statistics/1346736/ai-function-adoption-rates-business-finance/

1. Improved Decision-Making

Arguably, the biggest hurdle the finance department faces is not being able to make decisions quick enough. It is always waiting on entering and processing data or calculation not being ready. With AI, financial reporting and analysis could be done in real-time and with 100% accuracy, since no manual intervention is needed. This helps immensely when you want to seize opportunities and plan around risks. When done correctly financial planning helps in giving a leg up in the competitive market.

A travel management company used by Expedia – Egencia has been using AI since 2019. Their biggest pain point was faulty and delayed forecasting in an industry that changes rapidly. With machine learning, they developed a model that accurately forecasts based on the past 4 financial years of financial information. What used to take 100 people, executive reviews, still with the possibility of errors is now being done much faster and correctly.

2. Saving Costs and Less Errors

Automating repetitive tasks and incorporating AI to manage finance workflows directly brings down labour costs. AI helps management rearrange the workforce to focus on more premium tasks that involve creative thinking, decision-making, and assessment while automating mundane work. Indirect costs incurred from manual errors are significantly reduced too.

In fact, a survey by Statista found that 43% of the companies using AI in finance saw improved operational efficiency.

Take a cue from Microsoft – they have smartly bridged the gap between data and action and lowered the probability of human errors. When preparing quarterly forecasting, 60 personnel, and executive management with stakeholders, all used to be occupied for 2-3 weeks. With this model, only 1-2 experienced professionals are required to generate data in real-time and still is more accurate.

3. More Availability for Strategic Thinking

If an organization is making do with a small finance team, AI can benefit massively. When you automate redundant tasks that do not require critical thinking, you free up your employees’ time to dedicate towards tasks that require analysis and strategic planning. These functions factor in when considering decisions that can sometimes make or break a company’s future.

Even if you are operating with a bigger team, you can benefit from AI. Siemens reports that AI has improved their financial functions by 10%. They have now enabled interactive dashboards, accessible to all finance teams, that track trends and aid in making informed decisions.

4. Advanced Assessment to Prevent Fraud

Banks and big organizations are leveraging machine learning to run through millions of transactions to detect patterns, learn about fraud, and accurately predict it before losses occur.

Microsoft has deployed a combination of bots and machine learning where the AI automatically flags unusual transactions, and raises alerts, causing the financial controller to verify the finer details of the deals and whether it looks legitimate. This essentially saves the company millions and helps to catch wrongdoings before any real damage occurs.

Similarly, Bank of America also heavily relies on AI to reject fraudulent transactions by analysing IP addresses, account details, history, and other factors, potentially saving them billions by flagging questionable data beforehand.

Navigating Finance Analysis with AI: Embracing Challenges

When it comes to using AI in finance analysis, there are some challenges, but it’s still promising. AI is still new, so it needs more development to work well for everyone. Keeping data safe is a big worry, especially since rules about AI are still uncertain. And remember, AI is helpful, but it’s not perfect. People are still needed to make decisions and tell the full story beyond just organizing data.

The rise in AI has been sudden, and the technology is still very new. While it does solve many pain points for finance-based operations, more development is required to help organizations of all types.

Crash Course: Types of AI in Financial Analysis

AI replicates human intelligence to automate tasks, including those requiring manual intervention. Machine learning, a subset of AI, teaches machines to learn and make decisions from data without explicit programming. In finance, ML predicts fraud, price changes, and risks. Deep learning, a specialized ML technique, dives into vast financial data for tasks like credit scoring and algorithmic trading. Natural language processing (NLP) deciphers human language in finance, aiding in tasks like analyzing earnings calls and ensuring legal compliance.

Final Thoughts

AI will upend how a finance department works. There’s no question of “if,” but a question of “when.”

Experts are convinced that the AI revolution has just begun and in the coming years, the finance industry will change completely. It is predicted that AI will be advanced enough to make trading strategies and market predictions. Current functions will also get more sophisticated, key functions include real-time insights, enhanced reporting and visualization, vigilant risk management, advanced predictive analytics, increased automation, and NLP.

Whether you see this as a threat or an exciting opportunity is up to you. Change is coming fast, and the ones that move fast will get the first-mover advantage.