AI as a Service For The Financial Industry

AI as a Service For The Financial Industry
AI as a Service For The Financial Industry

AI as a Service (AiaaS) – ready-made Ai based tools that financial institutions, banks, companies, and other institutions use in their workflows for some fee. Often those tools are aimed at automating processes in the financial industry. 

Financial services refer to certain transactions with funds: 

  • money transfers; 
  • cash transactions; 
  • obtaining/issuing a loan; 
  • issue/sale of securities; 
  • loan/insurance registration; 
  • investments,  
  • guarantees, etc. 

Financial institutions provide services every day, intending to develop their activity. Moreover, they are constantly looking for technologies and solutions to improve their business processes. 

Today, it’s not a surprise anymore that most organizations process, analyze, and utilize big data using artificial intelligence (AI) and machine learning to 

  • assess investment opportunities, 
  • optimize portfolios, 
  • reduce risks. 

For example, automated financial advisors (or “Robo-advisors”) can help investors do all these without involving any human. Or another example: a Robo-advisor can help bank employees instantly determine a client’s solvency in the process of deciding to issue a loan. 

Of course, such virtual assistants are still far from being perfect, like any other financial technology. Still, FinTech is rapidly developing. And AiaaS providers constantly improving their tools took a significant role in this process. 

Let’s take a closer look at some AIaaS solutions and use cases. 

Improving employee efficiency through automation 

Artificial intelligence can generate business expense reports much faster than a specialist and with fewer errors driving value and minimizing risks. 

Also, AI automates such mundane processes as collecting, researching, and analyzing big data. And it helps employees to carry out the complex flows to check various protocols for anomalies. 

Better customer service 

High-quality customer service is an essential element of any financial system. The use of AI here led to the emergence and development of virtual assistants and AI interfaces that can reliably interact with clients without human assistance. 

The ability to automatically respond to simple clients inquiries cuts costs for a front office and support and improves response times, resulting in better customer service. 

Big data processing and analytics 

AI big data analytics tools are useful for the 

  • real-time monitoring of financial markets, 
  • trend research,  
  • collecting and processing open data about competitors.  

Moreover, AI tools can be used to track customers’ behaviors to identify warning signs of fraud. Likewise, claims management can benefit from using machine learning at various stages of claims processing, plus increasing the speed of processing claims to reduce costs and the overall processing time. 

Reliable credit ratings 

Many financial institutions still process loan applications without a complete picture of the applicant’s identity (or applying weak KYC procedure) – without checking his actual solvency and credit history. 

Existing AI solutions can collect data from potential borrowers to get an idea of their creditworthiness and create a client’s rating correctly. Such AI tools usually assess the client according to many additional parameters, without affecting the traditional indicators that form the credit rating like 

  • geographic location,  
  • web search history,  
  • profiles on job sites,  
  • posts on social networks, etc. 

Virtual assistants and chatbots 

AI-enabled chatbots use natural language processing (NLP) algorithms to learn from conversations with people and mimic language patterns when providing answers. Financial companies using these automation tools can provide information and advice to their clients even for each transaction. That allows a support team to focus on more complex tasks and cases. 

In addition, chatbots and virtual assistants can reliably perform a range of internal and external communications. These AI-powered chatbots and assistants are used by many traditional financial institutions such as American Express and HSBC. 

Financial forecasts and trading bots 

AI and in-depth market analysis seem like a natural match. Still, AI algorithms computing power and accuracy have been able to deliver predictions comparable to human experts only in the past few years. 

Existing solutions can evaluate companies using many variables, such as financial reports and public press releases, to analyze speech patterns, semantics, and word usage. As you can imagine, this can help eliminate human bias. In addition, machines can analyze big data over decades ahead and incorporate it into the forecasts. 

Another group of solutions includes financial bots that can effectively predict trend movements and automatically trade in financial markets. Such AI bots are often used on most digital exchanges and large investment companies, and not only for trading but also as robotic advisors. 

Compliance and audit 

Financial rules and compliance are constantly evolving. Even the largest financial institutions can find it hard to comply with laws and serve their clientele in an efficient and timely manner at the same time.  

Fortunately, AI can research and comply with laws that affect certain services, such as those governing asset management. In other words, AI gives companies of all sizes the ability to  

  • meet regulatory standards,  
  • significantly reduce the risk of human error,  
  • and detect abnormal activity. 

Minimizing the human factor 

Financial analysts experience various temptations affecting the accuracy of their forecasts. For example, they can make way more optimistic forecasts to win the favor of company management. However, the C-level may also be interested in a more optimistic forecast to attract more funds from investors. 

Artificial intelligence is unbiased, and it can be much more accurate in predictions because, unlike humans, it is impartial. And at the same time, human errors may be part of its algorithm, and then they will be reproduced systematically. 

AiaaS is an attractive and efficient approach for reducing costs and streamlining many of the workflows in the financial industry. One of the main advantages of AIaaS is that a company can try out various automation algorithms in the shortest possible time without spending hundreds of thousands of dollars on its own hardware and software development. 

Key benefits of employing AI as a Service 

  • The turnkey solution is easy to set up 
  • A choice from a variety of ready-made tools, the ability to test them and determine the most effective ones before the implementation stage 
  • It’s cost-effective – businesses can try out a ready-made tool before or instead of developing their own software 
  • No need to hire a team of experts and develop complex infrastructure 
  • Minimal dependence on staff and human factors 
  • Flexibility – AI tools can be tailored to business needs 
  • Transparency – the company only pays for what it uses 
  • Scalability – scale up or down based on business needs and growth 
  • Speed in customer service 
  • Availability of solutions based on complex machine learning algorithms for small companies and startups 
  • Competitive innovation advantage over companies that do not use AI in their business 
  • Out-of-the-box solutions provide an additional number of advantages, both in price and in a set of tools. For example, big data analytics, speech recognition and translation, voice assistant, and others. 


Financial companies can choose from a variety of AI out-of-the-box solutions, or select the right ready-made tool from one of the hundreds of AiaaS providers to get high-quality results with ease and almost instantaneous. Also, it’s always possible to develop brand-new AI algorithms, but the price and terms of this option will differ significantly when compared to AiaaS. 

All C-level and owners of financial companies need to understand that if the organization is not using AI today, it should start using it to stay in the market. The International Data Corporation (IDC) estimates that global AI spending will increase from $ 50.1 billion spent in 2020 to over $ 110 billion by 2024. 

Flexera’s report confirms this trend stating about the large-scale AI adoption by companies: 

  • 28% of businesses are experimenting with AI 
  • 46% of businesses are experimenting or planning to experiment with AI 
  • AI is the area where companies experiment the most 

What does this all mean?  

Undoubtedly, today’s solutions based on AI are already in the field of vision of most organizations. The main takeaway might be that the financial industry will use AI across the entire spectrum of financial services, and a ubiquitous transformation towards AI technologies will take place in the next few years.  

And it is most likely that AiaaS will play a key role there as the fastest, most cost-efficient, and high-quality solution for financial institutions.