Key AI Capabilities for Asset Management Applications

The global dynamics of AI development is impressive: investments in companies specializing in artificial intelligence reached $ 45 billion in 2020, an increase of 2.8 times compared to 2018. The technological arsenal of artificial intelligence is already impressive today. With some simplification, it can be divided into five large groups: machine learning, natural language processing, machine vision algorithms, software agents, or bots, and finally, robotics.

The use of these technologies, until recently passed in the category of science fiction, is effective now. AI makes decisions faster, making them more accurate and cheaper than humans. It is not for nothing that more than half of Swiss companies have already allocated budgets for the implementation of solutions based on in-depth analytics.

According to research by the McKinsey Global Institute (MGI), the financial sector, along with telecommunications and high-tech, is the most promising platform for the introduction of AI. The reason is a significant economic effect in working with clients, risk management, and increasing efficiency. Financial AI is already developing products for cross-selling and up-selling, optimizing pricing, reducing customer churn, identifying customers by voice or face, assessing credit risks, identifying fraudsters, predicting demand.

Many global banks are moving from single-use cases of AI to full-fledged implementation programs. According to McKinsey, more than 80% of large banks are already using these technologies. In #DACH, the trend is the same - projects for the implementation of AI are on the agenda of the heads of 50% of DACH banks.

Better than people

A typical example of the use of AI in credit scoring, which reduces losses to the largest players in the US auto lending market due to a more accurate assessment of the borrower's risk by 23-25%. In turn, one of the largest banks in the world is using artificial intelligence to recruit staff, benefiting from a more accurate selection of the optimal candidate.

A McKinsey study shows that lending institutions that implement AI across the organization have a cost-to-income ratio of 12 percentage points lower than the market average.

Seven Elements of Success

Based on the research, it was possible to formulate the main components of the success of companies implementing artificial intelligence. The first is corporate culture and willingness to work with new tools. A special role belongs to the management team: the chances increase if the top management is familiar with modern digital technologies firsthand. Successful companies prioritize AI projects. Another key to success is having the right organizational model. Successful programs necessarily include talent management - hiring employees with new competencies and developing existing staff.