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The rise of artificial intelligence (AI) in the finance industry has been rapid. AI is the new battleground on which finance companies are now competing.

The ultimate guide to AI in finance

The rise of artificial intelligence (AI) in the finance industry has been rapid. It is changing the industry’s landscape like never before. AI is the new battleground on which finance companies are now competing.

What is Artificial Intelligence?

Artificial intelligence or commonly known as AI is a term that blankets an entire branch of computer science focused on the thinking and learning capabilities of machines. Depending on the kind of data and information they are fed, the kind of training that they are put through, AI learns to make better decisions. This kind of ability to both learn and apply that learning to practical usage, is similar to the way human beings learn and apply that knowledge in the real world.

This means that machines can now accomplish tasks that were once possible only by a human mind. Some of these tasks include

  • Problem solving
  • Interpreting visual cues
  • Speech recognition/Natural language processing


These tasks are accomplished with the help of complex algorithms. These algorithms or intelligent programs can be run on various types of hardware or software. This results in a huge variety of use cases that AI solves for, making the subject of AI more difficult to understand.

The ability of AI to latch on to any kind of technology means that the scope for this field of computer science is immense.

How is AI used in the finance industry?

AI has various applications in the finance industry. These include


Let’s explore them in detail.

Credit decisions

Credit is a good thing, at least when it comes to finance institutions. A study by shows that nearly 77% of customers prefer using debit or credit cards to make payments as opposed to 12% who preferred to use cash. However, it isn’t the ease of payment that drives this preference. A good credit score helps customers in receiving favorable finance aid from finance institutions. Considering so much weighs heavily on a customer’s credit history, the approval process for loans and cards has become important.

How can finance institutions possibly hope to accomplish the approval process for numerous requests that come in each day, quickly, efficiently, and without mistakes? AI provides a faster and accurate assessment of the customer’s credit history using a wide range of determining factors. The assessment is therefore well-informed and backed by data. It also helps the lending company distinguish between high-risk applicants and those who have good credibility but lack credit history.

Managing risk

In the finance industry, time is translated as money. However, it is accompanied by high risk. And if it is not given proper attention, it might be fatal for the business. Finance companies should be able to accurately forecast profits and risks to protect the business from doom. Mere human skill and ability is no match for the challenges that the digital world throw at the companies.

Machine learning (ML), a branch of AI, allows finance companies to create models that use existing data to predict trends, identify risks, and provide information that helps finance experts in future planning. This makes AI in finance a powerful all.

Quantitative trading

Quantitative trading is the process in which large data sets are used to identify patterns in the stock market that can be used to design strategic trades. This is also known as algorithmic trading or high-frequency trading. It has risen steadily in the last 5 years and expanded across the world’s stock markets, closing in on one trillion dollars in 2018.

AI is particularly useful for this kind of trading or stock investment because of the speed at which it can crunch numbers and data and provide accurate predictions. It can monitor both structured and non-structured data in a fraction of the time it would take a data scientist. It outs together smart recommendations for the strongest portfolios based on the investor’s short or long-term goals. Automating trades means faster data processing and faster investment decisions. As mentioned earlier, time is money!

Personalized banking

Traditional banking is not good enough for today’s digital customers. A study by Accentureshows that 54% of banking customers 54% want tools that can monitor their budget and help them make real-time spending adjustments.

Virtual assistants and chatbots that are powered by AI provide customers various self-help solutions while reducing the cost of operating call centers. They also provide personalized financial advice using natural language processing (NLP). Voice-based virtual assistants powered by smart tech like Amazon’s Alexa are also quickly gaining traction fast. They sport a self-education feature which means they get smarter with more interactions with the user.

Cybersecurity and fraud detection

The world has seen a tremendous digital transformation in the last few years. This means everything can be digitalized is being digitalized. Thanks to the Internet, every day large quantities of digital transactions take place – right from fund transfers to check deposits – online or via smartphones. Hence it becomes important more than ever to have safety and security measures in place to avert fraud.

AI has been quite successful in helping finance companies battle fraud. Ai-powered fraud-detection systems can analyze customer’s behavior, location, purchase habits, and trigger a warning when something seems to contradict the customer’s spending pattern.

AI is also used to predict and prevent big scheme frauds like money laundering. Machines are well capable of recognizing suspicious activity. They reduce the cost incurred from investigating alleged money-laundering schemes.


Underwriting is a use case of AI in finance that is fairly nascent but it is expected to gain significance in the next few years. ML can be used by banks and insurance companies to assess whether an applicant is likely to pay back the loan or determine what the premium should be.

NLP allows finance companies to mine the applicant’s publicly available web activity. This enables banks and insurance companies to determine the trustworthiness of the applicant. AI is also be used by companies that sell property insurance.

AI can also be used for predictive and prescriptive analysis. Banks and insurance companies use the customer’s historical data such as loan and insurance payments, etc. to determine whether or not a loan or insurance policy can be approved.

Sifting through large databases of documents

It’s a struggle to search through large databases of digital documents to find the information they are looking for. Finance enterprises cannot afford to spend time in a monotonous activity when their team’s skill and time can be better utilized for other complex operations. With the use of NLP, finance firms can process large quantities of digital documents in a matter of seconds.

Search systems can now cluster paragraphs and different kinds of documents in a structured way. This allows the employee to quickly type in a keyword and find multiple documents that match their search intent.

Digitizing paper documents

One of the key challenges for finance companies looking to adopt AI is the availability of large volumes of historic data as paper documents. Since ML models are trained on digital data, this can be a huge roadblock for the company. They need to digitalize these documents before they can invest in an AI platform.

Machine vision software powered by AI and ML can solve this problem quickly. Employees can scan paper documents into PDFs and automatically upload them to the software. The ML algorithm runs through the documents and processes the information to automatically populate fields on a digital version of the uploaded documents.

Insurance claims

AI can be used to process insurance claims and optimize the claims process. Broadly, they can help insurance firms with the automation of the claim process, reduction of overpayments, and reduction of claim leakage.

Similar to underwriting, claim automation is a relatively nascent use case. However, it is more likely to be automated in the next couple of years.

In the case of reduction of overpayments and claim leakage, AI is used for predictive analytics and machine vision. It can help determine whether or not an insurer is about to make a payout which is perhaps more than what other customers have been paid for similar situations. The ML model is trained on historical customer data. The algorithm then correlates data points related to how a payout often results in.

Additionally, insurance agents can upload images of the damages that occurred to the insured object to the ML platform. The algorithms then assess the damage and suggest the sum that can be claimed. This speeds up the entire process for both the customer and the insurance company.

Document summarization

NLP is a great tool when it comes to processing information that is in the form of words, texts, phrases, and media files such as images and videos. It can be used by banks and finance institutions to summarize documents. It can also power up searches that employees need to run to find documents they require. It can take this process a step further and extract the particular piece of information from a document that is required. This provides granularity to read just the information that the employee requires instead of having to read through the entire document.


The above-mentioned use case of NLP-powered document summarization is particularly useful for compliance teams in finance institutions. Employees at finance firms can use NLP-powered tools to summarize large reports or documents to be presented at meetings with decision-makers. This saves them a lot of time.

Customer service

Chatbots are one of the most prominent tools powered by NLP and machine learning. They can be spotted on pretty much any website these days. They play the role of virtual sales or support agent. For finance companies, chatbots are more likely a low-hanging fruit, one that comes with AI capabilities.

Most chatbots allow customers to check their account information, address concerns, provide information on the next insurance or loan premium payment, etc. Chatbots are quite effective when addressing simple questions from customers. Since they can be trained on historical customer interactions, they have instant context regarding the customer’s query.

Will AI replace humans in the finance industry?

Interestingly that we humans are threatened by the very thing we are building. Hence, it is only natural that we ask ourselves the question – ‘Will AI replace us in the near future?’ The shortest answer to this question is ‘no’. Let’s explore this in detail within the context of the finance industry.


A study by the Cambridge Centre for Alternative Finance showed that AI is a crucial driver for finance companies in the short term with 64% of them expecting to become mass adopters in the next two years. This proves the potential growth of AI as a stimulator of innovation and growth across various business functions within the finance industry. Essentially, what we are seeing is mass adoption of AI by the finance industry, be it to improve existing products or services, or to innovate and add new products to the offering.

“AI is transforming the Financial Services industry and we can expect widespread adoption to continue. As the technologies give way to new revenue streams and transform business functions, it’s increasingly important for organizations to focus on the long-term implications of AI adoption.”

Nigel Duffy, Global AI Leader at EY

With finance companies racing towards AI adoption, it seems like people will soon be out of jobs. However, this isn’t the case. The same study showed that finance companies expect that AI will increase their workforce by 19% by 2030.


Where will AI create jobs in the financial industry?

Now that we know the positive impact AI will bring to the finance industry in terms of jobs, let’s take a look at which sectors within the finance industry will see this impact.


With AI in the picture, the approach to business processes in the finance industry will change (or has changed). This is not absolute – the change is ongoing. As the technology advances, advancements in the technology, finance companies will strive to leverage these advancements to, in turn, improve their businesses.

What are the challenges to AI adoption in the finance industry?

AI is a new technology and yet it has disrupted the financial industry swiftly. However, there are a few challenges when it comes to implementing AI for finance-related use cases, the most common ones being:

  • A lack of access to an accurate source of data. Data is central to the technology of AI and ML. Without data, there is no AI. When finance companies do not have the necessary data, then the company can make no progress.
  • Concerns over privacy – how will the customers’ data be used and how it will affect them.
  • The explanation problem or the black-box problem. It is sometimes also known as explainable AI. While many finance companies are using AI in their business process, it remains unclear how the technology functions.


Having said that, these are hurdles that the industry is trying to find a solution to. The pace at which AI is advancing, the answers to these challenges are not too far away.

Regulation of AI in the financial industry

The growth of modern finance services is associated with the ongoing advancements in technologies such as AI, ML, and NLP. Financial experts and supervisors view AI as an opportunity for the growth and development of financial services. However, every new technology presents risks along with opportunities. And AI is no stranger to this. It’s safe to say that the global regulatory agenda is still in its nascent stages.

Today, there is no AI-specific ‘law’. The regulations that are currently in place and the approach of the finance regulators/supervisors are neither encouraging nor discouraging the use of this technology. Based on the way these regulators have responded to the rise of fintech in the last few years, here’s what we can expect:

Technology-neutral approach: Finance regulators are expected to react within the existing framework of legal and regulatory laws.

Achieving a balance: Finance regulators aim to balance the risks and opportunities that AI presents when considering how to regulate the technology.

Embracing AI: Finance regulators have sought to embrace AI in their regulatory work.

Though this can be a good starting point for a regulatory framework, AI presents certain challenges such as resilience, ethics, accountability, and transparency.

Closing thoughts

The finance industry is one of the mass adopters of AI due to the various uses cases in which AI can be implemented promising high payoffs. AI as a technology is characterized by speed, accuracy, and efficiency. And at the heart of this technological revolution are ML algorithms that self-learn and improve over time. This is something that the finance sector can immensely benefit from. Though regulatory and compliance factors can be a deterrent to adoption, there is no stopping the finance industry from making progress using AI.

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