6 Tips for more success with machine learning projects

Artificial intelligence is seen as a key driver to make operational processes in the financial industry more efficient. Financial fraud detection is also considered an important field of operation. But often enough, machine learning applications don't deliver the results hoped for. What is the reason for this and how it can be better, the consulting firm cofinpro has fathomed.

Kunstliche Intelligenz eignet sich fur viele Bank-Prozesse – wenn man es richtig anpackt. [ZEBR_TAG_/p></p>
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<p>M arket analysts, management consultants and solution providers agree: with the help of artificial intelligence, the financial sector can leverage considerable efficiency potentials. Large volumes of unstructured data, such as those generated by banks, financial service providers and insurance companies, could be processed and analyzed in a standardized way using AI-powered systems. Machine learning, in particular, is predestined for it.</p>
<p>Accordingly, investments in AI technologies are expected to increase significantly in the following years. Mordor intelligence report estimated the global market for AI solutions in the fintech sector at $6.67 billion in 2019. By 2025, spending is expected to increase by nearly 240 percent to $22.6 billion.</p>
<h4>Little euphoria in practice</h4>
<p>However, if you look inside financial companies, there is little sign of this euphoria among users and IT managers – on the contrary. While 84 percent expected artificial intelligence to decide competition between companies in more and more subject areas. At the same time, however, around half of the 1.000 managers from the financial sector surveyed for the adesso AI report 2021 the AI capabilities of their own company (IT-finanzmagazin reported).</p>
<p>The consulting firm cofinpro (website ) also comes to the conclusion after market analyses and a survey of experts that the high expectations of financial service providers for the AI field machine learning (ML) have not yet been fulfilled. One of the central causes: lack of a high-quality, qualitative data basis. The advantages of a machine learning solution for financial companies are obvious:</p>
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