Generative AI is a powerful technology with a range of applications in finance and accounting. Designed to generate outputs like text and images, generative AI can automate financial processes, saving time and costs.
In this article, we’ll break down a few applications and explore how to integrate generative AI into finance business operations.
Generative AI has been underpinning finance and accounting innovations for decades. Several examples of generative AI-powered milestones in finance include:
Debuting in 1989 by Chase Lincoln First Bank (now a part of JPMorgan Chase & Co), the FICO score revolutionised credit scoring with its AI-powered approach.
The 1990s brought algorithmic trading to the forefront, pioneered by the Island Electronic Communication Network (ECN). Island ECN facilitated communication between market traders without using a broker as an intermediary.
The 2000s have seen the rise of sophisticated machine learning techniques used to identify fraud. Algorithms continuously monitor real-time transactions. When they identify patterns that deviate from user data and characteristics associated with fraudulent activity, they’ll flag these behaviours.
Chatbots became more widespread and effective in the 2010s. Financial institutions are leveraging chatbots to give customers access to answers to simple queries 24/7.
Despite some hitches, generative AI continues to automate mundane administrative tasks (like responding to customer queries) and more complex financial processes (e.g. algorithmic trading). Some of the advantages of AI innovation for financial services include:
Despite movements to use generative AI for more ambitious projects, AI remains the safest bet for financial institutions when they use it for specific, time-consuming tasks. Research shows that by 2027, financial institutions could even double their AI investments.
Automating financial processes saves time, freeing analysts and accountants to redirect their energy (and expertise) toward more strategic activities like the following.
AI process automation should generate clean, structured data, ready for in-depth expert analysis.
Highly skilled finance professionals can conduct more in-depth and sensitive activities, such as analysing data for potential compliance issues.
With more time, accountants and analysts can refocus their energy on building client relationships. To build trust and establish recurring business, Wealth and Finance recommends they become more proactive in initiating communication and setting up regular check-ins – difficult to do when they’re too busy copying information from PDFs and calculating simple ratios.
Financial and accounting institutions have plenty of incentives to experiment with AI besides cutting a bloated bottom line. Let’s examine four time-consuming processes that you can automate using generative AI.
Data extraction is a popular application of generative AI, according to multiple industry reports. From enterprises to small and mid-sized businesses (SMBs), businesses can benefit from receiving their data faster and more accurately.Older, outdated data extraction technology copies data from documents clumsily, often compromising its accuracy. Newer generative AI models, however, are far more sophisticated and customisable. You can choose the required data points, the format of the output and where in your system you would like the data deposited. PDFs or scans of financial documents suddenly become spreadsheets containing actionable information.Financial institutions and accountants can now extract data from documents like financial statements and invoices in real-time. Some companies will also have backlogs of historical data ready to run through a generative AI data extraction system. AI data extraction tools can reveal insights from huge data silos. For example, we helped litigation funder LitFin extract data from 300,000 invoices in under 4 weeks.
Let’s say you have a spreadsheet filled with extracted data. You’ve solved one problem, but now you’re facing another one. Instead of using highly-trained analysts to extract data from documents, you’re using them to calculate simple financial ratios. While calculating operating expenses or ratios from the captured line items only takes a minute, your analysts are spending that minute on a tedious task that is now automatable.New technologies can extract financial data and analyse it like a trained accountant to produce vital ratios, such as operating expenses (OPEX), EBITDA, etc.
Though only applicable to certain financial institutions, AI-powered loan underwriting is a highly rewarding application of generative AI, saving time and keeping operating expenses low. By extracting data from your uploaded documents, the data is then fed into a decision-making system, with any controversial points flagged for human review.That way, an AI-powered loan underwriting system preserves the best of both worlds – efficiency with the necessary human touch.
Like loan underwriting, KYC is a process applicable to certain financial organisations. Many companies delivering KYC use automation, as it’s an extremely lengthy process when completed manually. On the other hand, AI, offers the type of sensitive and contextual approach that is essential when examining human data. The first part of the process involves data extraction before the data is linked to a third-party source like Companies House for verification or enrichment. The data is then available for human review.
Integrating generative AI can be an intimidating prospect for any organisation. From investigating options to testing them, setting up the technological infrastructure and then training the staff, it may seem easier to continue using familiar manual methods.
Yet, as generative AI evolves, access methods become easier. Of course, there are still a few core considerations you need to know to make the most of generative AI’s time – and cost-saving benefits.
Firstly, it’s important to investigate the right prospects. Check their case studies. Have they worked on projects like yours? Ask the vendor how they will continue to adapt their technology in line with industry improvements.
It’s important to walk into your meeting with clear expectations. If possible, you might discuss how you would like your data to look, or how quickly you would like it to be extracted, etc. If you’re considering multiple vendors, compare their proof of concepts (PoCs) or trial services – and let the data quality speak for itself.
The time span of integrating AI will vary from weeks or even months (if meticulously researching and testing options). Alternatively, integration can take days if you lock in with a vendor that offers a fast setup to launch time. For example, we offer a time-to-launch of 24 hours, meaning that our clients can start accessing data without any delays.
Though integrating generative AI can be fast and effective, getting your team on board is essential when working alongside the right third-party technology.
The best way to make the most of generative AI in finance and accounting is to automate the repetitive processes draining your bottom line.
Generative AI can produce sophisticated outputs at a low cost when automating processes like loan underwriting and data extraction. Of course, generative AI (and the finance and accounting industries) will continue developing, so finding a ‘future-proof’ solution is essential.
Evolution AI’s new platform, Financial Statements AI, automates data extraction and analysis from financial statements, saving you costs and alleviating your administrative burden.
Would you like to find out more? Book a demo with our financial data team or email us at hello@evolution.ai.
