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How To Use Generative AI In Financial Services

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Generative AI (aka GenAI) exploded in popularity in early 2023. Through headline-grabbing ChatGPT, complex natural language-based queries could resolve issues related to content creation, creative ideation, website building, travel planning, image generation and coding. The possibilities rapidly became potentially endless. This made many people wonder how generative AI could improve their business workflows and processes, including finance operations. Let’s take a broad look at 5 top uses of generative AI in financial services.

5 Top Uses of Generative AI in Finance

1. Financial analysis and reporting

Generative AI can be a game-changer for financial analysis and reporting by automating tasks, extracting insights, and creating clear communication for different audiences. Enhanced efficiency and automation, deeper analysis and insights, and clearer, concise communication provide clear examples.

Data Gathering and Consolidation: Generative AI can automatically collect data from various sources, including financial systems, databases, and external sources. This eliminates manual data entry and reduces the risk of human errors.

Report Generation and Formatting: Generative AI can take the gathered data and populate financial reports like balance sheets, income statements, and cash flow statements. It can also format these reports according to specific regulatory requirements or company templates.

Identifying Trends and Relationships: Generative AI can analyse vast amounts of financial data to uncover hidden patterns and trends. This allows analysts to gain a deeper understanding of a company’s financial health, identify areas for improvement, and make data-driven decisions.

Scenario Modelling and Forecasting: Generative AI can be used to create simulations that model the impact of different economic conditions, market fluctuations, or business strategies. This “what-if” analysis helps assess potential risks and opportunities, allowing for better financial planning.

Customizable Reports for Different Audiences: Financial reports can be dense and technical. Generative AI can create clear and concise narratives that accompany the data, explaining key trends, variances, and insights in a way that’s easily understandable for different stakeholders. Different versions of reports can be tailored to investors, management, or regulators, for example, and thus improve communication and decision-making.

However, there are important limitations to consider.

  • While using generative AI in finance can automate tasks and provide valuable insights, human expertise remains crucial. Financial analysts still need to interpret the data, identify underlying factors, and make judgments based on their knowledge and experience.
  • Let’s also not overlook that AI’s effectiveness hinges on the quality of the data it’s trained on. Inaccurate or incomplete data can lead to misleading results and faulty analysis.
  • Overall, GenAI in finance can be a powerful tool to streamline financial analysis and reporting processes, uncover deeper insights, and communicate financial information more effectively. It empowers financial teams to focus on higher-value tasks like strategic analysis and decision-making.

How Tipalti uses AI

Using a finance automation provider such as Tipalti can be a game-changer for a company, particularly if it is experiencing fast growth or making international payments, through automating numerous tedious tasks. When implemented alongside a finance automation solution, generative AI models can help companies streamline operations, improve accuracy and forecasting, and substantially reduce the risk of errors and compliance issues.

Tipalti efficiently uses ChatCPT-4 to enhance the accuracy of expense coding during automated supplier invoice processing. Ask Pi, Tipalti’s digital assistant, lets users submit queries to use AI for Accounts Payable insights, and gain real-time business intelligence through all of its other finance automation products.

2. Fraud detection

Generative AI in finance is a double-edged sword for fraud detection. While fraudsters can leverage it to create deepfakes and synthetic identities, it can also be a powerful tool for financial institutions and businesses to combat fraudulent activity. While no system is 100% foolproof, here’s how.

Unearth Hidden Patterns: Generative AI excels at analysing vast amounts of data, including unstructured data like text and images, which allows it to detect anomalies and hidden patterns in user behaviour that might signify fraud. It can continuously learn and adapt, staying ahead of evolving fraud tactics.

Generate Synthetic Data: Generative AI can create synthetic but anonymized datasets of fraudulent transactions. They can be used to train fraud detection models more effectively, without compromising real customer information.

Real-time Transaction Monitoring: Generative AI in finance can continuously monitor live data streams to identify suspicious activity in real-time, allowing for immediate intervention and prevention of fraudulent transactions being completed.

Predict Fraudulent Behaviour: Through analysing previous fraud attempts, generative AI can learn to predict future ones. This enables businesses to proactively flag suspicious activity and implement preventive measures.

Improve Accuracy and Reduce False Positives: Generative AI can refine fraud detection models, leading to more accurate identification of fraudulent activity. This reduces the number of false positives, where legitimate transactions are flagged for review, saving time and resources.

Be aware that implementing generative AI for fraud detection requires significant computing power and ongoing maintenance. It’s crucial to stay vigilant as fraudsters themselves may try to adapt their methods to bypass AI-powered defences.

3. Expense management

Generative AI in financial services can be a game-changer for expense management by streamlining processes, reducing errors, and providing valuable insights.

Touchless Expense Reporting and Categorisation: Generative AI can work alongside Optical Character Recognition (OCR) to automate expense report creation. Simply take a picture of a receipt, and Generative AI in finance can extract details, categorize the expense, and even populate descriptions and account codes. This saves considerable time and eliminates manual data entry errors and misclassifications that could delay reimbursements. This is a step forward from crowdsourcing micro-taskers through platforms such as Amazon Mechanical Turk to transcribe receipts.

Anomaly Detection and Policy Enforcement: Generative AI can identify unusual spending patterns or potential policy violations, such as an employee expense that significantly exceeds typical amounts for a particular category. This helps ensure compliance with company spending policies and potentially uncover fraudulent activity.

Predict Spending Insights: Through analysis of historical spending data, it is possible to forecast future employee spending trends. This allows businesses to create more accurate budgets and optimise cash flow management.

Improve User Experience: GenAI can create a more user-friendly expense management experience. It can answer employee questions about expense policies, guide them through the reporting process, and even suggest ways to optimise their spending habits.

Overall, generative AI automates tedious tasks, minimizes errors, and empowers businesses with valuable insights into their spending patterns. This translates to cost savings, increased efficiency, and better control over finances.

4. Taxes and financial compliance

Overall, generative AI in finance holds immense potential to transform how we handle taxes and financial compliance. By automating tasks, identifying opportunities, and managing risks, GenAI can make the process more efficient and accurate, less time-consuming, and potentially save taxpayers money.

Automating Data Processing: Tax preparation involves a lot of data gathering and organisation. GenAI can automate this by sifting through receipts, bank statements, and other financial documents, extracting relevant information, and populating tax forms. This saves accountants and other individuals significant time and reduces the risk of errors.

Understanding Unstructured Data: Many financial documents are unstructured, like emails or contracts. GenAI can analyse this data to identify relevant tax implications, saving professionals from manually combing through mountains of text.

Identifying Deductions and Credits: Generative AI can be trained to recognize patterns and trends in past tax filings. This allows it to suggest potential deductions and credits that taxpayers might have missed, ensuring they claim everything they are entitled to.

Risk Management and Audit Preparation: GenAI in finance can analyse financial data to identify potential red flags that could trigger an audit. By proactively addressing these issues, taxpayers can minimize the risk of penalties and streamline the audit process.

Personalised Tax Planning: analysis of a taxpayer’s unique financial situation and goals can lead to tax-saving strategies. This could involve investment options or retirement planning tactics that optimise tax benefits.

Limitations

Once again, there are important considerations to keep in mind when using generative AI in finance.

  • Whilst GenAI in finance offers significant advantages, tax laws are complex and ever-changing. Generative AI should always be regarded as a tool to assist tax professionals, not replace their judgment and expertise.
  • Financial data is clearly highly sensitive. When using generative AI for tax purposes, organisations should ensure robust security measures are in place to protect user information.
  • This cannot be repeated often enough – generative AI is only as good as the data it’s trained on. Inaccurate or incomplete financial records can lead to errors in tax calculations and potential compliance issues.

5. Financial planning

Generative AI, with its ability to analyse data and create new content, can be a powerful tool to improve financial planning in several ways.

Enhanced Forecasting and Budgeting: By crunching historical data, generative AI identifies patterns and trends, generating more accurate predictions for future income, expenses, and savings. This allows better allocation of resources and planning for different market scenarios through simulated modelling. A “what-if” analysis helps anyone to understand how different choices might affect long-term goals.

Personalised Financial Advice: Gen AI in finance can analyse an individual’s financial situation and goals to generate personalised recommendations. This could include suggesting investment strategies, optimising a budget for debt repayment, or identifying areas where to cut back on spending.

Risk Management: analysis of financial data to identify potential risks, such as market downturns or unexpected expenses. This early warning system allows taking proactive steps to mitigate these risks and protect financial security.

Efficiency and Automation: automating time-consuming tasks in financial planning, like data collection and report generation, frees up human time to focus on making strategic decisions and managing finances more effectively.

In the age of generative AI, financial planning requires human judgment and a consideration of personal risk tolerance and goals. However, generative AI has the potential to revolutionise financial planning by making it more personalised, efficient, and insightful. As ever, it remains important to remember that generative AI is a tool, and is only as good as the data it’s trained on.

Key Takeaways of AI and Machine Learning for Finance Automation

By leveraging the power of generative AI models and finance automation software, companies can make better and more informed decisions, and substantially reduce the risk of errors and compliance issues. While there are undeniable limitations to using the public iterations of generative AI such as ChatGPT, we highly recommend that to ensure the safety and security of sensitive information, businesses should reach out to reputable providers that have already implemented natural large-language models within their hosted software.

Tipalti was our Boldest Marketplaces category partner in the 2024 BOLD Awards. Check out the full list of BOLD Awards V winners.

Clive Reffell

Clive Reffell

Clive has worked with Crowdsourcing Week to source, create and publish content since May 2016. With knowledge and experience gained in a 30+ year marketing career based in London, UK, he helps SMEs and startups to run successful crowdfunding projects, and provides support across wider marketing issues.

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