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AI Technology in the Financial Industry

July 7, 2020

The financial industry was one of the first to appreciate the benefits of Artificial Intelligence (AI) technology and put it into service. The annual budgets of large banks amount to billions of dollars – this is comparable to the state budgets of some developing countries. The actions of large banks can have a significant impact on the value of shares of the world’s leading technology companies, the services of which they use.

Artificial intelligence confidently penetrates into all spheres of life. The AI market is growing at times annually. First of all, the financial sphere, retail segment, and industry influence its growth. The growth of hardware performance and the increase in the volume of big data have a positive effect on the development of machine learning systems and neural networks. Businesses are ready to delegate increasingly complex, intellectual tasks to such systems.

Artificial Intelligence in the financial industry

AI has been helping banks around the world detect fraud for several years, replacing assistants, and can replace personal financial advisors. The need to constantly reduce costs and increase profits forces traditional retail banks to invest and collaborate with artificial intelligence companies. Banks hope that this move will help them stand out from the crowd of competitors, introduce innovative services and solve the growing fraud and money laundering problems, equivalent to billions of dollars lost annually.

For example, in a study of disruptive technologies from a non-profit organization of retail banks, EFMA back in 2017 showed that banks see the greatest threats from technology companies and active candidate banks in the competition – 48 and 47%, respectively. At the same time, banks react to trends and invest a lot in innovation and technology, and AI was also seen as a key component of future changes. Moreover, then about 74% of banks said that working with innovative startups, suppliers or partners was on their agenda.

AI Technology

Nowadays, financial companies continue to gradually implement AI, using it to analyze data, automate complex tasks, comply with regulatory requirements and improve customer service.

According to a study conducted by the Forrester research team in 2019, half of insurance companies and financial services around the world already use Artificial Intelligence, and it is expected that as new applications of the technology are discovered, their number will increase.

Morgan Stanley’s International CIO, Catherine Wetmore, said the following: “We are only scratching the surface of the potential that AI has for the industry.”

The financial industry has long been using AI to analyze stock market data and machine learning to improve fraud detection. For example, Morgan Stanley bank, also uses AI as virtual assistants for customer inquiries, to detect fraud and “sentiment analysis,” which measures how negatively or positively an analyst thinks about a company’s stock.

Ping An insurance company customers in a car accident can use an artificial intelligence-based smartphone application to photograph the accident and receive an insurance offer to fix the damage within three minutes. Insurers also use artificial intelligence to analyze large amounts of data, identify anomalies and relationships, making it easier for the insurance agent to determine if a claim could be fraudulent.

For small fintech companies such as Trezeo, this technology is used to tailor services to customer needs. It uses AI to analyze customer spending and income to help them manage their finances.

Now the financial industry has begun to pay more attention to:

  • Business Process Robotics

Large financial companies, such as some banks and stock brokers, already use Robotic process automation in the most important organizational processes. More and more large banks are automating the filing of documents and preliminary screening of potential customers. RPAs are also being implemented in real-time transaction tracking in accordance with counterparty identification requirements. Robotization now extends to medium-sized financial market players: small hedge funds and private investment companies are introducing RPAs both in internal operations – training new employees, compliance control – and in customer service processes.

So far few have managed to attend to the full-fledged implementation of software robots, and they tried, first of all, to connect systems and processes in different departments of their organizations. Automated processes were more often initiated by a person, but the situation is changing – RPA mechanisms are now much more often launched on the basis of emails or data taken from the database.

Artificial intelligence plays an increasingly prominent role in automation. From the very beginning, financial institutions used natural language processing (NLP) mechanisms to automatically extract data, but now they are turning to machine learning with much greater success.

  • Mobile insurance

Insurance companies will try to offer users highly personalized products. At the same time, the barrier to entry of new players into the market will decrease.

Companies will need better tools to process a huge amount of personal data, and to ensure access to them from customers’ smartphones, they will have to update outdated systems and architectures.

The unpredictability of the business environment will lead to an increase in interest in on-demand solutions, in particular, PaaS (platform as a service) and cloud services.

  • Smart chat bots

Artificial intelligence and chatbots managed by it are increasingly taking the place of live consultants in remote banking. They help customers register and manage accounts, arrange loans, and complete transactions. Now chatbots should be brought to work as investment advisors.

Artificial Intelligence
Artificial Intelligence

Challenges to Innovation

However, the implementation of AI is not without problems. The most important is the lack of qualified personnel. According to the study, 30% of the financial world only heard this term, but did not understand how AI works. And today, the whole industry faces an important task – to increase the level of technical literacy.

The second serious problem is the lack of data for work. The more initial data, the higher the accuracy of AI predictions: with a small sample, the probability of error is 20%, with a large array – up to 2%.

New technologies – new risks

When introducing new tools, the company is faced with risks that had not previously been encountered in its practice. They can lead to financial and reputational costs. This raises the legal question of liability in the event of an error: who will be to blame – a financial specialist or an AI developer.

Consider a practical example. A trained algorithm may not always be able to avoid bias. Thus, according to a historical sample, in recent decades, women have been less likely to approve loans. And, based on the data presented, the algorithm will conclude that women are unreliable borrowers, and will refuse even creditworthy ones. The bank may face claims from regulators who see gender-based discrimination in these decisions.

The introduction of AI and machine learning will scale financial systems. This is relevant, given the projected increase in the number of financial transactions until 2025. A person simply cannot handle this amount of information. But this does not mean that AI will force a living specialist out of the financial sector. If the algorithms will be engaged in routine operations, then the employee will always have the final control and live communication with customers.

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