Artificial Intelligence in Finance Technologies

Today we will discuss about finance AI and Artificial Intelligence in Finance technology

Artificial Intelligence in Finance Technologies

The use of artificial intelligence in fintech is qualitatively changing the industry. AI-based technologies allow financial market participants to make real technological breakthroughs in terms of security, personalization of services, business modeling and many other areas of activity, which were discussed by experts and representatives of companies that are leaders in the development and implementation of AI technologies at online events held in early December : the international conference on artificial intelligence and data analysis AI Journey – 2021, as well as the international webinar “Artificial intelligence in finance. Experience of Eurasia.

Trends and Solutions

According to the analytical materials of the Center for Expertise of the federal project “Artificial Intelligence”, prepared on the basis of data from Rosstat for 2020, it is fintech companies that are most actively implementing AI solutions, ahead of trade and the ICT sector, which occupy the second and third places, respectively. At the same time, the situation in the world looks somewhat different: fintech shares the second and third lines with the automotive industry.

If the processes of banking activity are conditionally divided into three blocks, then the first block can be divided into management processes: these are strategic management, management of banking products and marketing, public relations, branch network development, distressed assets, risks, finance, quality and personnel.

AI has been actively used here in such systems and services as customer scoring, business forecasting, customer segmentation, automatic robocalling, cash forecasting at ATMs and collection planning, analysis of the location of offline points.

The second block is the main banking processes. It includes servicing retail and corporate clients, working with financial institutions, as well as activities in the stock, financial and derivatives markets. The list of main AI solutions for these processes includes chatbots, voice assistants, as well as personalized electronic services and personal financial assistants.

The third block of banking combines supporting processes. These are ACS, legal and IT support, document management and accounting, security and internal control, measures to counter the legalization of proceeds from crime and the financing of terrorism.

In this block, artificial intelligence is actively used in biometric identification technologies, in document recognition, fraud monitoring and detection of atypical financial activity.

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illustrative examples

To understand why the use of AI is becoming a global trend in the digitalization of the financial sector, it is enough to consider a few typical examples of the use of AI in fintech.

AI-assisted customer scoring reduces application approval time from days to minutes. The cost of scoring is reduced, and its quality is growing, and thus affects the amount of delay.

AI in voice assistants is, firstly, intelligent call routing within the call center. And secondly, it is communication with the client through a voice assistant inside applications. Today, he independently receives up to 80% of calls in intelligent mode and automatically processes 10% without contacting a person. The service time for each customer decreased by an average of 40 seconds. If the voice assistant is implemented correctly, then in the voice queue a person waits much less, and if he does, then he is routed to the correct employee with the correct point request.

ai voice

Another example is smart chatbots. These are omnichannel means of communication in which the activity of a living person is imitated. Today, 60% of customer requests are completely or partially closed by bots automatically. The average time to solve problems on customer requests was reduced by four times.

Also, biometrics and anti-fraud are among the brightest examples demonstrating the benefits of using AI in fintech. The first one makes it possible to remotely open bank accounts and conduct financial transactions remotely, reduces the time for their implementation, while the accuracy of customer identification grows many times. And the use of AI to detect atypical activities makes it possible to stop about 7 billion fraud attempts nationwide every year, thereby saving the funds of both banks and customers.

Key development technology

Tatyana Zharkova, CEO of the FinTech Association (AFT), spoke about a study conducted jointly with the international consulting company Accenture on the opinion of representatives of the financial market and related industries on the development of financial technologies for 2021-2023.

The survey participants were the largest Russian banks, payment system companies and insurance companies. They agreed that artificial intelligence is one of the key technologies that will seriously affect the development of the financial market in the near future.

The main number of participants in the survey in the banking sector – about 95% – answered that they are already using AI-based cognitive technologies today.

At the same time, if you look at the insurance industry, you can see a big gap here: 58% of the companies surveyed do not use AI today. However, they all note that in the long term, these technologies will allow the insurance market to create new products and find solutions for further development.

AI evaluates satisfaction

The quality of customer service is one of the most sensitive components of the business of financial institutions. It is not surprising that this area was one of the first to be automated, both in terms of communications with customers and in terms of assessing their satisfaction.

According to Bain and Co, a 5% increase in the number of customers gives more than a 25% increase in profits. Microsoft in its research shows that 61% of customers change brands due to poor customer service, and this trend continues to this day.

AI reduces risk

One of the leaders in terms of the introduction of artificial intelligence today is VTB. In particular, the bank is among the main domestic ideologists of data fusion.VTB, spoke about the trends in the use of artificial intelligence that the bank is focusing on in the near future.

The first area is scoring. Today, the adoption of credit scoring decisions is serviced by robot models, without which it is generally impossible to form a loan portfolio. And here comes the issue of operational risk management, which is directly related to the topic of responsible use of AI.

In accordance with the requirements of the regulator, banks need to set up high-quality monitoring of the models that make these decisions. But the problem is that the risks for the portfolio that the model forms are triggered as it matures – in a month or two, or even three. And this is a direct loss of the bank. To avoid them, VTB began to develop the use of the MPP approach (Model Performance Predictor). It allows you to train a special monitoring model on a special sample, that is, AI begins to control itself and predict a possible deterioration in performance.

The second trend that VTB is actively developing concerns the very important area of ​​User Experience (UX). These are all kinds of personalized investment advisors. The use of AI in them lowers the entry threshold for private investors, minimizing unnecessary risks for clients. This trend is already on the hype, and the bank is confident that this hype will continue to grow.

Artificial Intelligence in Finance Democratization for efficiency

 There are five steps towards the democratization of banking technologies

  1. First of all, and this is the first step, you should decide what to automate, why, and what effect you want to achieve. Hyper-automation, an effect that the industry has achieved through a large number of solutions that allow you to quickly apply point automation of certain processes. At the same time, due to the growing number of such tasks, their intersection, the whole picture often looks inefficient. To understand this, artificial intelligence comes to the rescue, which allows business processes to be covered in their entirety.
  2. The second step is the improvement of the existing automation infrastructure. Once you have decided on the tasks, you should understand where exactly the use of AI tools will be most effective.For example,text analytics technologies.It is obvious that there are a huge number of areas of activity in banking where it is possible to effectively implement text processing technologies in natural language. For example, to open an account, a lot of documents are still required, and so far there are no prospects for their reduction.AI understands the meaning of documents and helps to make decisions on them ten times faster.
  3. Increasing efficiency through the democratization of technology is the third step : We are talking about the use of fast low-code / no-code solutions, driven by growing digitalization around the world, customer experience, competitive requirements for the timing of the launch of products on the market and cost reduction, as well as modern trends in personnel management.
  4. Step four is risk reduction by reducing business process variation and error reduction. Solutions of the Process Mining class, which are a family of methods for processing data from the analysis of operational processes based on event logs, are very helpful in this. As well as Task Mining technology, which helps to record and analyze the activities of employees, automatically identify repetitive actions in any business processes and analyze whether they should be entrusted to software robots.
  5. And the final step, which is dictated by the competitive environment, is the continuous improvement of business processes by updating technologies in order to further improve the quality of customer service.

Conclusion

AI banking technology is no doubt next generation banking & finance technology.It will save banking service cost as well as perform more fast.The only things we need to evaluate whether it has any bad impact on human life.There are lot of things are still need to upscale before we switch on and depends on Finance AI.

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