AI-Powered Automation in Finance: Key Trends and Use Cases

The gap between traditional and technology-forward banks has never been larger than now. One bank may take thirty minutes to onboard a single customer after having them wait in a queue for two hours while another can get the process done in minutes through a simple mobile app.

One of the main driving factors in this divide is automation technology that can take full advantage of artificial intelligence (AI) and machine learning (ML). This is because AI-powered automation tools have drastically lowered the time needed for most processes while also enhancing compliance and even bolstering the user experience.

In this article, I want to take a closer look at AI-powered automation, see how it is affecting the finance world, and discuss the use cases where this technology has had an immense impact.

Why is AI automation so impactful in the finance sector?

The main reason why AI automation has been so impactful for finance is because it allowed for key processes to be done more quickly while remaining compliant and accurate.

According to a survey performed by the Bank of England and Financial Conduct Authority, 75% of firms are already using artificial intelligence in 2024. Given that the financial world is typically rather averse to change, such high adoption rates point to just how disruptive these new technologies have been.

For one, even traditional automation solutions benefit financial institutions in major ways, such as:

• Making back office processes significantly faster
• Lowering human error rates
• Keeping processes consistent across different use cases
• Offering a better customer experience
• Lowering employee frustration and churn

However, when this already impressive technology is coupled with AI or machine learning, both the benefits and the use cases grow exponentially. Namely, AI automation can enhance key processes such as:

• Fraud detection
• Client Lifecycle processes
• Data analysis and forecasting
• Client onboarding
• Compliance checks and reporting

Just to name a few. With all of these processes being essential for staying compliant, keeping overhead low, and staying competitive, it shouldn’t be a surprise that the financial world has taken special interest in AI automation technology.

Key trends in AI-Powered automation for finance

As AI has advanced, it has become more specialised, with different industries, branches, and processes being catered to by different models. And while the list of AI trends will only continue to grow, I want to look at a few notable examples of AI and automation in the finance space.

1. Intelligent Process Automation (IPA) in banking & finance

Robotic Process Automation (RPA) was a significant step in the digitalisation of the banking sector as businesses could automate certain repetitive tasks, such as data reconciliation or basic customer service processes.

Intelligent Process Automation (IPA) was the next step of development, as it combined RPA with new technologies such as Optical Character Recognition (OCR) and AI. This significantly increased the number and complexity of tasks that could now be fully automated.

For example, an IPA system can use OCR to read hand-written forms, structuring the data and pushing it further in the pipeline where it can be logged. This in turn means that processes such as customer onboarding, loan approvals, and periodic KYC can be largely automated.

2. AI-powered workflow optimisation

A significant downside of traditional, hard-coded systems is that making changes can be rather difficult. Furthermore, said changes will typically have to be made retroactively.

However, with the introduction of AI, and by using real-time data analysis, banks have a lot more control over their workflows. Not only are these systems a lot more flexible, they can typically adapt in real time in accordance with the data presented. 

This is of vital importance when performing client risk assessments, claims processing, compliance checks, and similar processes.

3. Conversational AI for automated customer service

From the point of view of the consumer, chatbots are the most obvious development when it comes to automation in the finance sector. This is because they drastically improve the customer experience when it comes to simple requests as they are always available and easy to use. 

To these ends, in Switzerland, 65% of people polled state that they prefer chatbots when it comes to simple issues. However, the complexity of tasks that these chatbots can handle is rapidly expanding with developments in AI.

For example, by using robo-advisors, users can get tailored financial services, such as portfolio management. Furthermore, since this service is automated, the service fee will typically be noticeably lower than if talking to an actual advisor.

4. Automated compliance

Smart automation systems enhance compliance efforts in many ways. For one, automation helps standardise processes across different departments, products, and jurisdictions. This furthermore helps keep human error at a minimum, preventing non-compliance due to negligence. Lastly, simple automations can be set up to keep detailed audit trails and make straightforward reports.

But more advanced models can do a lot more than just that. By using AI to detect pattern anomalies, financial institutions can also significantly bolster their monitoring process. Plus, given that AI can process vast amounts of data, risk analysis and KYC checks also become a lot more robust.

5. Vendor and process orchestration

Lastly, powerful automation software can be used to orchestrate different processes and third-party vendors to ensure the entire tech stack is functioning efficiently. This is especially important for the finance sector as more businesses are opting for multiple different best of breed solutions instead of just one central software.

In this context, orchestration software can be used to trigger the appropriate vendor or workflow at the right time. For example, when performing KYC, different KYC vendors may have better or worse coverage depending on the jurisdiction in question.

Therefore, a bank can set up a clever automation that, when onboarding a new customer, sends a request to the appropriate vendor depending on the country said person is from. But that’s just one process. These kinds of tools will typically manage dozens or even hundreds of different vendors and processes, reacting to data as it’s presented and choosing the best option accordingly.

Real-world use cases of AI-powered automation in finance

Automation and AI have made their way into every corner of the finance world. However, not all financial institutions will get the same benefits from the same models. Therefore, in the following section, let’s look at a few real-world examples of how different financial institutions might utilise AI-powered automation tools to enhance key processes.

1. AI automation in retail banking

Retail banks will arguably see the largest benefits from automation and AI tools right away. This is because even medium-sized retail banks still need to process hundreds of transactions, onboarding and loan applications, and compliance reports.

Therefore, even just utilising IPA to fully automate the onboarding process will significantly lessen the load for the bank in question. Furthermore, while not a direct benefit of AI automation tools, retail banks that invest in technology can also transition to digital environments more easily, allowing them to acquire and manage more users.

Example: By using Atfinity’s AI-powered automation software, HBL Bank UK made their onboarding 80% faster while also ensuring compliance with AML/CFT regulations.

2. AI in wealth & asset management automation

AI automates portfolio management by continuously analysing market conditions and rebalancing assets to align with clients' investment objectives and risk appetites. Given the vast amount of data available and that AI is always on, this is an invaluable tool for wealth and asset managers.

Furthermore, its AI tools can develop dynamic investment strategies based on predictive analytics, optimising returns. All the while, AI also ensures compliance by monitoring transactions and flagging activities that deviate from regulatory standards, reducing the risk of non-compliance.

3. AI-powered automation in corporate finance

Identifying, assessing, and mitigating financial risks, such as market volatility, credit default, and operational disruptions, is a critical but complex task. Traditional risk management approaches often struggle to process vast datasets swiftly, limiting their effectiveness in predicting and responding to emerging threats.

This can however be remedied to an extent with the use of automation and AI. Namely, AI can be used to create a plethora of realistic stress tests to gauge key metrics in niche or unpredictable circumstances. Not only does this data allow for more well-informed risk management but it can also be used for more advanced forecasting and other similar processes.

Additionally, AI automation can be used to more efficiently close smaller M&A deals without putting more strain on your team.

Example: UniCredit has generated approximately 2,000 leads and secured 500 mandates by allowing AI to take the wheel for deals valued below €50 million.

4. AI-Enhanced claims & insurance processing

The integration of AI in claims processing offers numerous benefits, including faster claim settlements, improved accuracy, and enhanced fraud detection. This is done by using larger data sets, limiting manual data entry and using AI to detect pattern anomalies.

But we’ve seen AI accomplish more complex tasks in this sector as well. 

Example: In auto-insurance, AI can be used to analyse images of the car in question in order to estimate repair costs accurately.  

By automating routine tasks, AI allows insurance professionals to focus on complex cases requiring human judgment, thereby improving overall operational efficiency.

Benefits & challenges of AI-powered automation in finance

Now that we’ve covered all the things that AI and automation tools can do, let’s quickly go over some of the benefits as well as challenges that come with this technology.

Benefits

• Increased efficiency and scalability - with AI automation, banks can optimise all of their key processes, react to market changes and integrate new systems very quickly.
• Reduced operational costs - automation allows banks to handle a lot more clients with a much smaller staff.
• Enhanced accuracy and risk mitigation - with fewer manual tasks, human error becomes much less of an issue.
• Faster processing and improved customer experience - by design, automated processes will always be faster which in turn means that customers are less likely to be frustrated.

Challenges

• AI bias in automation - if the AI model isn’t trained correctly, it can propagate certain biases in decision-making.
  
• Integration challenges with legacy financial systems - in the absence of orchestration software, properly integrating AI models into more traditional tech stacks can be difficult.
  
• Regulatory concerns - data protection and AI specific regulations, such as the AI act, might considerably restrain what AI can be used for.
  
• The need for human oversight in AI-automated decision-making - fully straying away from a human-in-the-loop design can come with some negative consequences.
  

The future of AI-powered automation in finance

So, what comes next? I think that for the most part, the future of AI automation in the finance field will boil down to polishing up what is already there. Namely, making it easier to integrate AI into different tech stacks, making the decisioning more transparent, training AI on higher-quality data, and striking a balance between complete automation and a human-in-the-loop design.

Similarly, regulations will have to come into effect to fully address the usage of this technology across different fields. We’re already seeing regulators catching up in some areas such as the EU but others are likely to follow in the near future.

But there will likely also be new innovations in the field. For example, perfecting self-learning AI automation that can be used for even more accurate market forecasting and even things such as transactional monitoring or loan origination. And I also think that generative AI will start having a more significant impact in finance for things such as contract creation.

Conclusion

Automation marked a big step in finance, AI an even bigger one, so it stands to reason that AI-powered automation has made a noticeable impact. As this technology develops, it will be capable of tackling more and more complex tasks, utilising vast amounts of data, anomaly recognition, self-learning and reactive automation to completely streamline key processes across the financial landscape.

If you want to leverage AI to automate processes such as onboarding, loan origination, or KYC/KYB, book a demo and see how impressive this technology can be.