The world of financial operations, particularly in enterprise-sized organizations, is undergoing a revolutionary transformation. And the torchbearer of this change? Artificial Intelligence (AI). We’ve seen various forms of AI take shape and be used in business for years now, starting with machine learning, moving on to scripted automation and on Generative AI with platforms like ChatGPT.

From predicting market trends to optimizing customer onboarding, automating invoicing to risk assessment, know your customer to contract generation and processing, AI is not only enhancing efficiency but also redefining the way businesses approach their financial operations.

Let’s dive in to understand why and how enterprises should integrate AI into their financial functions, and the essential considerations they should be cognizant of.

Why Use AI in Financial Operations?

Efficiency & Accuracy

The fact is that AI can process vast amounts of data at astonishing speeds, reducing or streamlining the tedious manual tasks that are prone to human errors. We call that reducing the drudgery of day to day operations.

Things that the enterprise currently has entire teams to do, like account reconciliation, processing invoices, contract processing, and customer onboarding can be handled in seconds or minutes rather than hours or days. In fact, we’ve seen use cases where Charli AI has helped teams reduce time to completion on high value projects from around 15 days to just a couple of hours.

Predictive Analysis

AI can help financial forecasting move from being reactive to proactive. True AI can analyze data across a wide variety of sources simultaneously and find trends in the market and provide insight to make decisions and draw conclusions on.

How Can AI Be Used in Financial Services?

There is a wide range of use cases for AI in the enterprise — including some which don’t even include working with numbers or market data.

Back Office Optimization

Financial services is a paper-first industry and the information on that paper is critical to the business. This often requires people to manually extract the content from these documents and populate it into other systems. With extractive AI and OCR tools, we can now leave that to AI which reduces human interaction time and can significantly increase accuracy.

Client Onboarding

Client onboarding often means compiling customer data and information from a huge range of sources, analyzing that paperwork, and finally getting the information into relevant financial systems. Any delays in onboarding will directly affect revenue and customer experience. AI can take onboarding down from weeks to minutes.

Market Research

Using automated research is a smart way to get more done. Analysts spend so much of their time sourcing market data and relevant news articles that they use to create insight and strategic recommendations from, but what if AI can do that for you? AI allows analysts to spend less time searching for data and more time actually synthesizing that information and making recommendations.

Regulatory & Compliance

We have seen so many non-regulated industries adopt AI, but solutions like Chat GPT just don’t cut it in the enterprise and especially in financial services. While generative AI like this is impressive and has a use, the fact is consumer-grade tech like this isn’t trusted or reliable enough to be used.

Keeping pace with ever-changing regulations is a daunting task and can present risks to both the operations and reputation. Regulations are heavy in Natural Language paperwork and AI can make short work of understanding, breaking-down and synthesizing regulatory changes across the globe.

Equity Research

Automate the tedious and time-consuming exercise of keeping tabs on changes within the equity markets. AI can alleviate the burden on analysts by discovering, collating and presenting data to allow them to make informed decisions and streamline report generation.

Why Does AI Matter?

Staying Competitive

These days more and more organizations are employing AI to streamline their operations, speed up customer service, and do market research. It’s truly a time where if you’re avoiding AI then you’re going to be outpaced by those that are actively using it.

Cost Savings

By automating repetitive tasks, organizations can redirect resources to more value-added activities, saving costs in the long run.

Informed Decision Making

Decisions can’t be made in a vacuum. And with AI, we’re able to make decisions which are informed by up to date information and data as well as information from a wide variety of sources which are difficult to acquire without automated support. AI allows decision makers to base strategies on real information and data, not just anecdotal information.

Considerations About AI for Enterprise Financial Services

Security

Business leaders have to ensure the systems used in their business are aligned with best practices and are secure. For AI to be trusted in the enterprise it must be secure. This means the AI needs to be closed to the public, and the data you feed it should never be accessed outside of the organization. Additionally, your private data should never be used to train the AI or be accessed outside of your organization. Consumer grade AI solutions like Chat GPT cannot offer this.

Explainability

It’s extremely important that generative AI can explain how and why it comes up with the answers that it provides you. Otherwise you are just given responses and then left in the dark about how that solution came to be. In the world of AI the term black box means that there is no visibility into how the AI comes to solutions, where the source data was derived, or why the AI output what it does. This may work for some use cases like personal projects or small needs in unregulated industries, but in financial services this will not do.

AI needs to be fully traceable, trusted, and verifiable, meaning the output can be traced back to source data and the AI should be able to easily show you how and why it provided you with the results it did.

Domain Specificity

Just as it is important to hire human employees with specific training and skillsets to do highly specific or regulated work (financial advising, legal, etc), your AI models should also be trained to handle these highly specific skills. It’s a time consuming and expensive endeavor, but something you should ensure.

Privacy

Data is gold when it comes to AI. AI needs data — and a lot of it — to make informed decisions and provide valuable output and insights. Organizations must ensure that AI systems comply with data privacy laws and that customer data is anonymized and protected.

Ethics and Bias

AI systems, primarily when used for decision-making, must be free from biases. Regular audits and updates are necessary to ensure fairness and transparency.

Conclusion

AI is already in use in a pretty major way all around the business world. We’re using it to create art, write content, and automated routine and complex tasks. It’s a game changer when it comes to allowing humans to elevate and focus on priority work and not on the drudgery of day to day operations.

But in the enterprise and particularly in the world of finance, consumer grade AI platforms like ChatGPT don’t measure up to security protocols, compliance, and regulatory needs. This has historically meant financial services organizations either need to build their own AI solutions or hire a high-priced development team to do it for them.

With Charli AI, the enterprise now has a better option. Work with us to implement an AI that is proven to provide amazing solutions and cost savings to your organization.

Charli AI is a fully transparent, trusted and responsible AI for the Enterprise. Get a Demo today.