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Machine Learning And AI Use Cases in Banking and Finance in 2023


13 min read

Innovative technologies are slowly taking over the world, being present in nearly every market segment and disrupting customer’s expectations by exceeding them. Consumers are so used to AI that sometimes they don’t notice how often they actually use it. That being stated, around 97% of mobile users surveyed by Finances Online used AI-powered voice assistants, and 51% using voice assistants in their cars.

We’ve gotten accustomed to the idea of a virtual helper so much that it’s hardly possible to imagine a day without Siri or Google-assistant. And, thinking about the business-side of this, machine learning capabilities prove extremely useful in risk management with about 82% of surveyed business owners using the technology for this purpose according to Finances Online. The same research also revealed that process automation (61%) and performance analysis and reporting (74%) can benefit from AI and ML, too.

So, this article focuses on how you can adopt AI in finance technology business and benefit from it in order to make your enterprise or startup more efficient and customer-centered. DashDevs gathered the most interesting and practical AI use cases so read on to learn more about disruptive innovations.

How Does AI in Finance Work?

Artificial intelligence and machine learning are causing a huge fizz, and everyone knows how it works in theory from mainstream movies and fiction. Despite being as far from the truth as possible, they hold some value in relation to the general principles of AI’s work. So, we’ve got a complex technology that is capable of self-learning and hence advancing the conventional workflow of businesses in nearly every domain across the globe. But how can it apply to fintech in particular?

How can AI be used in Fintech?

So, let’s bring it down one by one. There are a few ways AI and machine learning in finance can enhance the business processes, decision making, profitability, efficiency and customer relations. In the image above you see the most widespread of them all.

  1. Cognitive computing

Cognitive computing stands for AI systems that imitate the process of human thinking, such as real-time environment analysis, context analysis, and other forms of problem solving. Among the most useful features cognitive computing can bring to the finance are the following:

  • Enhanced data analysis achieved by gathering and processing unstructured data from various sources;
  • Advanced security using the analysis algorithms that focus on the behavioral pattern of a customer and can warn them about any suspicious activity from their account;
  • Algorithmic trading with cognitive computing can mitigate the risks via automated analysis and make predictions based on larger amounts of data, ensuring higher returns.
  1. Natural language processing (NLP)

NLP is a popular tool present in our lives for many years, transforming our way of living. This market segment grows rapidly with projected value amounting to and exceeding $127 billion dollars by 2028. No doubt, you’ve already seen the benefit of such tools, for example in your smartphone. Language translations, voice-operated GPS, digital phone calls, predictive typing, and virtual assistants like Siri, Alexa, and Google Assistant all use NLP technology.

Since NLP tools possess wildly sophisticated and immensely similar capabilities for imitating human communication, it’s vastly used in chatbots and client support. Speech recognition and word sense disambiguation allow the technology to imitate human communication good enough for customers to feel reassured. Tools like these can answer the frequently asked questions without making a real customer support worker engage in the interaction, allowing them to focus on other tasks.

Along with this, NLP can be used to advance the security system and counteract money laundering. Tools of artificial intelligence in finance such as the NLP tools can improve optical character recognition (OCR), discovering and analyzing more information about the customer, tracking their financial behavior. The same instrument, but with machine learning (ML) instead of OCR for deeper analysis can perform fraud detection taking it to the next level with advanced analytics.

  1. Machine learning in finance (ML)

Machine learning is characterized by an ability to analyze enormous amounts of data in a short period of time. This gives the technology a benefit of optimizing the workflow’s outcome and improving data-based predictions as well as decision making.

There are a lot of options available for machine learning in the fintech segment, however some of the most fruitful for businesses we will be covering in this paragraph.

The first one is robo-advisors, a loud and relatively novice solution aimed at enhanced data analysis for providing financial advice. They are often used by investors instead of human portfolio managers’ services as they are more cost-effective.

Machine learning is also highly capable of enhancing personalization in fintech applications. With this branch of AI implemented into the software, it’s possible to provide users with customized advice regarding spending behavior, give them recommendations according to their recent searches and bought items, and inform them of more beneficial deals. About 94% of mobile banking apps customers would prefer to get informed about the improved and new deals via the application, and 27% would like to get personalized advice via the app.

So, cognitive computing, NLP, and ML in finance can disrupt the industry turning tables to your side.

According to Artur, VP of Research and Development at DashDevs, artificial intelligence should be perceived more as a benefit to a company rather than a necessity.

However, innovative solutions nowadays are seen more as mandatory improvements rather than competitive advantage by many, but the way you put it to action can change the way people perceive your business. That being said, if you’re looking to implement innovative technologies you will most likely face confining challenges, and you better be prepared for them.

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Challenges of Implementing AI Solutions in Finance Industry

As any obstacles, challenges of AI implementation require understanding and knowledge from your side. Don’t feel obligated to become tech-savvy, however you need to understand the technology and what you might face working with it.

Limited budgeting for artificial intelligence in banking

AI being an innovative technology is a large investment, so budgeting is a natural argument that arises with the need to implement it. An AI implementation in reality is more complex than simple integration of a third-party service or a new feature development. It’s an innovative project which requires financial input in order to work correctly, and startups might face the need to make it more budget-friendly.

By all means, cutting costs on the development or hiring is not an option. Rather, if your project is fresh and you’ve just started, try working on a minimum viable product (MVP) first.

In 2021, the AI investment ranks got to the all-time high record amounting to $93.5 billion with around 52% of companies surveyed by McKinsey&Company pouring more than 5% of their digital budget in AI in 2022.

MVP in turn allows you to attract investors to your company, and investors nowadays are interested in projects powered by innovative technologies as data above shows.

AIs can be biased if created or taught by biased humans

AI technology is self-taught but it uses materials created by humans in order to learn. Hence, if the said materials are biased the AI will learn some unwanted and unseemly patterns. There are some examples of such behavior demonstrated by artificial intelligence as one of the loudest cases of racism demonstrated by the US healthcare system AI.

The software favored white-skinned patients over POC because it considered an individual’s past healthcare expenses. Although the AI was amended to reduce the bias by 80%, this mistake was costly and deprived people from getting equal medical treatment.

Amazon hiring AI algorithms were also biased with favoritism towards men in software engineering positions. AI in financial services might demonstrate the same inadequacy in RegTech, mortgage issuing process, etc. if not handled properly.

So, how to fix a malfunction like this? DashDevs has some solutions that might help you figure it out.

  1. Filter the data your AI algorithms use to train. Artificial intelligence is designed to automate the tasks and it’s frustrating that it takes so much manual work to set it right. However, once you do the job, the algorithms would work much better, saving you time in the future. Provide your AI with various and unbiased data to analyze, testing it in a way you will be using it further. And don’t forget to test those algorithms post-deployment.
  2. Try human-in-the-loop systems. It implies that you would have to gather continuous feedback from people about the output of the AI’s work. This will curate the biased information as a result creating more accurate and effective output that neither human staff or machine learning algorithms can achieve on their own. The unity of technology and human mind’s flexibility could allow you to achieve precision impossible to gain otherwise.
  3. Use synthetic data. While it’s important to configure your AI algorithms to work under real-life constraints, real-life data is still extremely biased and can’t be the only source for AI to learn from. There are startups and companies creating synthetic algorithms which are statistically representative of real-world data. It is not an ultimate solution since it can raise more problems while erasing others, but it is a convenient approach to consider.

Lack of AI engineers

Artur, VP of Research and Development at DashDevs, commented on the topic from the technical point of view. According to the expertise DashDevs has with ML technology, some engineers might encounter challenges and complications in sequence of sample testing. However, if you have a reliable technical partner on your side, this problem will be sorted out.

According to AI Multiple, about 59% of surveyed companies viewed the lack of data science talents as the primary barrier for the technology adoption. This challenge grows even more crucial, as according to Deloitte, most companies view the skill gap in meeting the needs of their AI projects as moderate. So, how can you fight this obstacle and hire qualified professionals?

Outsourcing or staff augmentation could be the best answer for both small and large companies, including startups and enterprises. The reasoning behind this is the access to a world-wide talent base which opens a way to hire people from different companies and countries, with different backgrounds and narrow yet flawless expertise.

Hiring in-house teams can also be a blow on your budget, while staff augmentation allows you to be flexible working with specialists part-time or on a short-term basis. Outsourcing in turn takes away most of the expenses you’d need for an in-house team and allows you to estimate the development costs with more precision.

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So, with this taken out of the way, let’s check out the companies that serve as a successful example of top machine learning and AI finance use cases.

Artificial Intelligence & Machine Learning Use Cases in Finance for 2023

Theory is good but it’s much more effective, interesting, and inspiring to see other businesses pivoting with the technologies you’re about to explore. So, to inspire you, we’ll discover together the loudest startups and established companies that use artificial intelligence finance solutions.

Kasisto–an example of AI-powered customer experience

The first thing that comes to mind at the idea of AI-powered customer experience are advanced chat-bots, and that’s no wonder. In 2023, chat-bots saved banks 862 million hours, and more and more people are starting to advance from this technology.

Key AI in fintech statistics–chat-bots segment

Companies are deploying these solutions in order to free up time for their employees to get about the crucial tasks or to cut costs. But chat-bots also come with enhanced customer experience and greater profitability, and Kasisto showcases it the best.

Founded in New York, this series C startup has general funding that amounted to $81.5 million. The product in question is KAI, an artificial intelligence that enhances customer experience and lowers the traffic for contact centers. AI-powered chatbots drastically help customers in finance decisions acting like assistants, offering advice for advanced financial literacy and decision making.

Vectra–enhanced AI-driven fraud detection

Security and fraud detection is a major pain point for the fintech segment, and having AI and ML algorithms can help resolve most of the issues associated with it. Companies often turn to these technologies in order to ensure their customers’ data is protected.

For example, Juniper Research found out that fraud losses might exceed $343 billion total over the next five years. The numbers are peaking which makes customers trust less in online banking, even though they find it more comfortable. Advances in financial machine learning might be able to fix the said issue and Vectra’s product Cognito demonstrates it the best.

San Jose-based startup Vectra with over $352 million in total funding developed a technology that is capable of identifying and pursuing cyber threats using AI and ML algorithms. Fintech companies can leverage by adopting the technology since it provides automated threat detection along with the ability to pinpoint the covert attackers aimed at financial institutions.

Hazy–the example of synthetic data used for an AI training

As we’ve stated above, artificial intelligence isn’t a panacea for all troubles and should be carefully guided in order to avoid biased conclusions. So, synthetic data can help fintech software train AI algorithms to be more adequate and responsive to the customers’ requests.

Mistakes made by ML/AI technologies can occur in different segments of fintech software. Here are only some of them:

  1. Biased loan and mortgage issuing;
  2. Biased insurance issuing;
  3. Biased information gathering for financial advice and behavior analysis;
  4. Biased insurance portfolio compilation.

All these might have a strong effect on both your company’s productivity and image, so it’s important to battle the AI bias by all possible means. While synthetic data is only one way to get over with the struggle, Hazy, a UK-based startup, demonstrated how highly efficient it can be.

They promise their customers a number of benefits from reduced risks and eluded data breaches to higher revenue and faster innovation pipeline. Among their customers are established enterprises like Vodafone as they accelerate the AI adoption and empower business intelligence. For one of their customers, Hasy helped increase an annual subscription fee by 20%.

Future Opportunities of AI & ML In Finance

It’s hard to overestimate artificial intelligence in finance and accounting since the impact the technology has on this domain is immaculate. The opportunities for machine learning finance services are growing each year, and not only that but the technology is often used in different segments one way or another connected with fintech

AI across business domains

As the graph demonstrates, AI technology is being widely adopted in client acquisition, risk management, revenue generation, and other segments that are crucial for fintech business as well. So, while it is obvious that AI and ML finance software will disrupt the industry, you might still wonder about the vector of its growth.

So, DashDevs’ research concludes that the most popular ways for companies to adopt artificial intelligence will but not limited to be the following:

  1. Financial advice and behavior analysis;
  2. Easing the load on employees via automated services, allowing them to focus on more vital tasks;
  3. Customer support and experience;
  4. Customer acquisition and personalized experience;
  5. Customer and other data analysis;
  6. Enhanced security;
  7. Faster transaction and other operations approval;
  8. Enhancing cost-efficiency of work planning and improving the decision making process;
  9. Enhanced fraud detection;
  10. Algorithmic trading.

Final Thoughts

However you decide to use artificial intelligence or machine learning technology, it is a powerful tool when handled right. Future holds a lot of challenges and innovation, novice ideas and disruptive startups that will achieve successes with AI. The interest in this technology is peaking, and it’s a perfect moment to ride the wave.

DashDevs can be your technology partner to help you go through this journey. Get on a consultation with us today and skyrocket your business with top-tier talents and clear code that exceeds every expectation.


What is the example of machine learning in finance?

Machine learning is capable of executing complex data analysis. One of the most interesting machine learning examples in finance is advanced analytics that recommends customers certain services or items based on their behavior. ML algorithms can also improve fraudulent behavior by analyzing customers’ behavioral patterns.

What is an example of artificial intelligence in finance?

Artificial intelligence is a self-educating technology that can learn to imitate human thinking. The most popular use case of AI in finance are chatbots that help customers get answers for those questions that don’t need human intervention to manage them. Such solutions free up staff to work on more urgent tasks and improve customer experience.

How are AI and ML used in finance?

There are numerous cases of AI and ML being used in financial software. The most popular of them, however, according to DashDevs are the following:

  • Financial advice and behavior analysis;
  • Easing the load on employees via automated services, allowing them to focus on more vital tasks;
  • Customer support and experience;
  • Customer acquisition and personalized experience;
  • Data analysis;
  • Enhanced security;
  • Faster transaction and other operations approval;
  • Enhancing cost-efficiency of work planning and improving the decision making process;
  • Enhanced fraud detection;
  • Algorithm trading.

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