Survey Reveals AI/ML Usage by 76% of Top Financial Firms

76% of top financial firms currently use advanced technologies as AI/ML usage soars

Machine learning (ML) and artificial intelligence (AI) are powerful technologies that are transforming the digital revolution through several businesses and sectors; they are no longer just buzzwords. To improve customer satisfaction, operational effectiveness, and performance, the financial sector is one of the most inventive and active users of AI and machine learning.  The world's leading financial firms are adopting AI/ML Usage in various ways. This is according to a recent report by Accenture. 85% of the 1,000 executives who participated in the poll, which represented 15 countries, said they intended to boost their investment in artificial intelligence and machine learning (AI/ML) during the next three years, while 76% of users reported using it already. 

The survey also lists the main forces behind the use of AI/ML in the financial sector, as well as its advantages, disadvantages, and best practices. The following are some of the survey's key conclusions and revelations:

• Adoption of AI/ML is primarily motivated by the need to enhance customer experience, boost income, and cut expenses. According to the study respondents, they may accomplish a range of business objectives with the aid of AI/ML, including improving customization, boosting customer loyalty, creating new revenue streams, optimizing pricing, cutting down on fraud, and automating procedures.
• Increasing decision-making, innovation, and competitiveness are the three key advantages of adopting AI/ML. According to the survey participants, AI/ML can enhance their ability to make better decisions in areas like credit rating, risk management, and portfolio allocation. They can also promote innovation by developing new goods, services, and business models with the aid of AI and ML. Additionally, AI/ML can give them a competitive edge by boosting their market share, customer happiness, and reputation.
• Ensuring data security, governance, and quality are the three biggest obstacles to the implementation of AI/ML. The responders to the study agree that for AI and ML to work efficiently and accurately, high-quality, trustworthy, and diversified data are necessary. Risks to security and privacy from AI/ML include identity theft, cyberattacks, and data breaches. In addition, ethical, legal, and regulatory compliance, as well as explainability, transparency, and accountability, are necessary for effective governance and monitoring of AI/ML.
• Adopting a collaborative, strategic, and all-encompassing strategy is one of the primary best practices for implementing AI/ML. According to survey participants, implementing AI/ML should not be viewed as a stand-alone or isolated project, but rather as part of the larger business strategy and vision. Adoption of AI/ML should also include coordination and collaboration between many teams, stakeholders, and departments, including data, IT, business, and AI/ML specialists. Adoption of AI/ML should make use of outside platforms and partners, including cloud providers, vendors, and consultants.

According to the study results, the financial industry is transitioning from trial to adoption and scalability of AI/ML solutions, with AI/ML emerging as a critical enabler and differentiator. The poll does, however, also emphasize how important it is that the sector faces the risks and problems that come with AI/ML and takes a responsible and moral attitude to its development and application.

The survey's findings indicate that while AI and ML are useful tools that can supplement and improve human intelligence and capacities, they are not a panacea. The survey underscores that AI/ML is an opportunity rather than a danger to the financial industry, as it allows it to better serve its clients, staff, and the community.