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Use Cases and Examples of RPA and AI in The Finance Industry

Automation AnywhereBusinesses

The finance industry is welcoming automation software more and more each year, given its scale, this comes as no surprise. The amount of data and analysis necessary to stay competitive in the market has very quickly gone beyond human capabilities, so companies have turned to Artificial Intelligence (AI) to help them crunch the numbers and make the best decisions. This article will go into some of the areas where AI and Robotic Process Automation (RPA) have been thriving in the finance industry.

Credit and risk management

When it comes to credit, AI solutions have been most useful when it comes to helping banks and other financial institutions with making underwriting decisions. The biggest advantage of AI is the power it has to collect, process, and analyze data on a scale that would be far too complicated and time-consuming for humans to do.

RPA and AI are particularly useful when it comes to credit decision-making due to their ability to collect and analyze data. With credit being more used than ever before, creditors have to deal with more and more requests from new customers, Many of which have a credit history, but many are new and starting with a blank slate. This creates an overwhelming amount of work for financial institutions to go through. Assessments can take a long time to do manually, and creditors want to get through them as fast as possible to maximize their approvals. At the same time, they want to minimize the number of potential customers that are risky and will end up defaulting.

This is where AI comes in. AI can do quantitative analysis at a level that is incomparable to human beings, going through thousands if not millions of data points and drawing quantitative conclusions from them. This is particularly useful for customers with a previous credit history, as AI-powered tools can quickly assess a customer’s creditworthiness based on their previous history.

For new customers, things can be more complicated because there is a lack of data surrounding them. Thankfully AI can be used to do a demographic analysis of things that aren’t directly related to a person’s credit but can give creditors insight such as location, age, income, profession and education. In isolation, these don’t mean much, but when analyzing hundreds of thousands of people, these tools can produce predictive analyses that can help reduce the risk of bad approvals for creditors.

AI doesn’t even stop here. We have only begun to scratch the surface when it comes to AI-powered quantitative analysis. Looking at demographic factors for new customers is an innovation, but solutions can go much further than this. AI solutions aren’t only used in the finance industry, they are used in any field that makes use of large-scale quantitative data. Finance-focused AI and RPA solutions are constantly learning from the innovations made in other fields, and using their discoveries to find new methods of analysis, new data points to track, and new ways to represent data.

Quantitative trading

Quantitative trading is the practice of making trade decisions with the help of large swaths of data. More simply put, it’s about using software to find patterns in the market that will help you make more informed and more profitable trading decisions. AI is an obvious certainty for this area of the finance industry given the complexity of trading at large scales. With the size of the industry, it is practically a necessity to use automation to assist you in decisions around trading.

There are a variety of ways AI-powered tools can be used here. The most straightforward one is just automated quantitative analysis of the market to streamline trading. AI can look at many more factors than humans can, and can analyze complex layers of data much more reliably and quickly. The best thing about automation is that, to some extent, it ignores biases that humans are susceptible to.

For example, a human might skew their calculations and projections based on their preferences for a certain stock. Maybe they just really want that stock to do well, so their analysis will lean towards demonstrating that. While AI isn’t without bias, it definitely won’t bring with it an emotional attachment to any specific thing when looking at the data you provide it with. Using AI to do the bulk of your number-crunching will result in analyses that are closer to unbiased than anything a human could produce. Pair this with how dramatically quickly you can scale AI-powered analyses, and businesses quickly end up with a much more solid quantitative foundation to stand on when it comes to trading.

One part of trading is that it is extremely layered and can often be hard to navigate. There are many factors to keep track of in general, and this isn’t only in regards to data. The amount of jargon that can come with market analyses can be overwhelming because so many different fields and industries have an impact on the trading market.

One way AI tools can be used is to translate complex ideas into approachable and understandable language. AI tools use natural language processing to be able to understand not only numbers and data but also words and sentences. AI that is trained in this can create much more understandable reports for executives to better understand every minor detail and nuance that went into the analysis.

It is extremely valuable to have the option to let AI process unstructured data points and give humans actionable results to make simple decisions with. Not only does it save a lot of time and labour, but it makes decision-making more reliable and less stressful. AI can organize immense amounts of data and scale it down to something very simple, going from thousands, if not millions, of data points to a simple graph and a prediction of performance makes a huge difference for the employee that will have to make the decisions at the end of the day.

The best part about AI is that you can even use tools that will make decisions for you. AI-powered tools are basically pattern-seeking machines, and many tools will not only crunch data for you, but they will analyze what decisions you make based on the data the tools provide you with. After some time, machine learning helps these tools understand how to make these decisions themselves. This means that you can streamline decision-making processes even further. Predictable and repetitive decisions can be progressively automated, leaving only the most complex and nuanced decisions to be made by humans.

Personalized Banking

Personalization has been a huge part of the world of business: from sales to marketing, to customer support, everything is dominated by personalization because that’s what the customer likes best. The financial industry is no exception to this trend. The old way of banking is falling out of fashion, as consumers are getting more and more used to hyper-personalized services outside of the finance industry. If financial businesses want to stay competitive and keep up with the personalization trend, one of the best things to do is to use AI and RPA to automate some aspects of personalization.

Typically we think of AI and RPA as tools that businesses use behind the scenes – number crunchers and graph generators that can help executives decide how to shape their products and services better for the customer. This is far from the limit for AI and RPA. Automation can be customer-focused, where it is used to build the best experience for the customer rather than the best product: AI-powered personalization.

One of the most important places to offer personalization is in customer service. RPA can be used in customer service to learn from customers when they call into call centers. Most call center queries fall within predictable and repetitive categories. AI-powered solutions that use machine learning will be able to categorize and understand what customers need when they call based on this.

Businesses can use AI to automate most call center queries, saving valuable human labour. This helps manage call center volume because AI can handle virtually infinite cases simultaneously. At the same time, it helps reduce frustration and fatigue that may occur for call center employees, as they will avoid repeating simple answers to calling customers. Instead, the AI will handle those queries, and whenever things are more complicated and unique, the queries can be transferred to employees to deal with them.

AI can also be used to integrate mobile and online banking into everyday aspects of life. With AI becoming more and more present in daily activities with tools such as Google Home and Amazon Alexa, the finance industry is looking to use AI solutions to integrate their own processes into these all-in-one solutions to help customers feel more comfortable with their banking.


Many tools focus on providing the client with data and analytics, or with a streamlined experience for customers. There is one more area that makes great use of AI and RPA, and that is cybersecurity. With the astronomical number of transactions and requests taking place in the industry, pinpointing fraudulent or dangerous activity can be perilous at scale.

AI-assisted cybersecurity has many advantages over traditional cybersecurity. The first is that it uses machine learning, so it is constantly improving on its own. While improvements and updates from humans will be necessary, machine learning helps keep protective software ever-evolving and versatile.

The second is effectiveness. If your cybersecurity process is fully automated, you’ll have fast response times to threats, leaving no room for any dangers to materialize. Automated software doesn’t see a breach as a high-pressure situation, it will operate as it is programmed to. This severely reduces the risk of mistakes while at the same time decreasing response times.

The third is that AI-powered cybersecurity can allow us to spot new patterns, new dangers, and new responses to issues that were previously invisible to the human eye. With the amount of data that an AI can process, it can discover fraudulent activity or security weaknesses in areas that a human could never expect to. Paired this trait with machine learning and the result will be a secure foundation for any financial company to work with.

Find the Right RPA System For You

The right automation tool can help organizations in all business sectors minimize risk by ensuring better management of business operations with RPA. Not only will it help to reduce errors and mitigate risk, but it will also increase the compliance, scalability, and dependability of your organization.

At K2 University, we’ve partnered with industry leaders Automation Anywhere to offer RPA Training for Finance Professionals. Our Mastering Bots course offers best practices, certificate guidance, web data management, and chatbot development – find out more.

Ottilie Wood

Ottilie has worked with K2 since 2019. Over that time she has specialized in content creation to engage our world-class clients and customers.

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