AI and ML tools such as data analytics, data mining, and natural language processing, help to get valuable insights from data for better business profitability. The finance industry, including the banks, trading, and fintech firms, are rapidly deploying machine algorithms to automate time-consuming, mundane processes, and offering a far more streamlined and personalized customer experience. AI helps the financial industry streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management. In the financial services industry, 94% of surveyed IT professionals said they aren’t confident that their employees, consultants and partners can safely protect customer data.

The financial sector is well-known for seeking every possible edge to maximize its profits — thus, using machine learning and artificial intelligence was a no-brainer. Utilized by top banks in the United States, f5 provides security solutions that help financial services mitigate a variety of issues. The company offers solutions for safeguarding data, digital transformation, GRC and fraud management as well as open banking.

2. AI and financial activity use-cases

A plethora of use cases is leveraging the power of artificial intelligence — from fraud detection, risk assessment, improving customer satisfaction, increasing accounting and transactional automation to algorithmic trading. While this kind of specialized chatbot experience is not the norm today in the banking or finance industry, it holds great potential for the future. This is one application that goes beyond just machine learning in finance and is likely to be seen in a variety of other fields and industries. When it comes to banks and financial institutions, data is the most crucial resource, making efficient data management central to the growth and success of the business. There are various budget management apps powered by machine learning, which can offer customers the benefit of highly specialized and targeted financial advice and guidance. Machine Learning algorithms not only allow customers to track their spending on a daily basis using these apps but also help them analyze this data to identify their spending patterns, followed by identifying the areas where they can save.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers. The value of AI is that it augments human capabilities and frees your employees up for more strategic tasks. Oracle’s AI is directly interactive with user behavior, for example, showing How Is AI Used In Finance a list of the most likely values that an end-user would pick. Companies that take their time incorporating AI also run the risk of becoming less attractive to the next generation of finance professionals. 83% of millennials and 79% of Generation Z respondents said they would trust a robot over their organization’s finance team.

Use of artificial intelligence in accounting and finance

However, a recent report by WEF reveals that AI will eliminate 85 million jobs by 2025 but will also create 97 million new jobs at the same time. Therefore, this calls for reskilling and upskilling to avoid being rendered obsolete by AI and to prepare for the new era of work. AI brings with it its fair share of benefits and drawbacks, especially for the financial world.

With so much information publicly available and increased fraudulent activities, organizations are finding it increasingly challenging to keep their usernames, passwords, and security questions safe. A recent article from Deolitte introduces a UK-based robo-advisor, Wealthify, which is considered one of the fastest growing robo-advisors in the market today. It’s based on an in-house algorithm that recognizes and anticipates changes in market conditions and automatically proposes shifts in clients’ investment accounts, and sends a push notification to the client. The robo-advisor tends to make investments to maximize returns within an acceptable level of risk through diversification. The general information that the robo-advisor needs includes age, investment timeline, and risk tolerance. All technical analysis is based on statistical data, market behavior, and past correlations.

Which Industries Are Benefiting the Most When it Comes to AI in Finance?

Because of improvements in artificial intelligence, financial industries’ disruptive capacity to influence traditional financial institutions is expanding. Apart from commercial banks, a number of investment banks such as Goldman Sachs and Merrill Lynch have also integrated analytical AI-based tools in their routine operations. Many banks have also started utilizing Alphasense, an AI-based search engine, that uses natural language processing to discover market trends and analyze keyword searches.

AI, integral to the bank’s processes and operations, and keeps evolving and innovating with time without considerable manual intervention. AI will enable banks to leverage human and machine capabilities optimally to drive operational and cost efficiencies, and deliver personalized services. By adapting AI, leaders in the banking sector have already taken actions with due diligence to reap these benefits.

Loan and credit decisions

CNBC revealed that the average debt of an American citizen stands at $90,450, with the amount increasing annually. ‌As AI establishes its presence in the world of finance, it brings with it numerous impacts—both beneficial and detrimental. Data science use cases, tips, and the latest technology insight delivered direct to your inbox. Taking all of the above into account, it is no wonder that Harvard Business Review famously named data scientists the “sexiest job of the 21st century” . Head over to the on-demand library to hear insights from experts and learn the importance of cybersecurity in your organization.

For instance, Danske Bank, Denmark’s largest bank, implemented a fraud detection algorithm to replace its old rules-based fraud detection system. This deep learning tool increased the bank’s fraud detection capability by 50% and reduced false positives by 60%. The system also automated a lot of crucial decisions while routing some cases to human analysts for further inspection.

Corporate Finance

Since then, OCR has made its way into enterprise resource planning and customer relationship management , going far beyond check processing. OCR was created by MIT researchers to quickly and accurately read and match the handwritten portions of checks, and effectively changned the perception of using AI in the banking industry. For example, when bank employees give biased advice based on AI recommendations, the entire institution may start systematizing bias into the decision-making process.