Financial services companies can leverage AI to increase productivity, improve operations and customer service, reduce risk, enhance security, and more. However, some challenges need to be addressed when implementing this technology.
AI has significantly transformed finance’s operational landscape, from machine learning to Natural Language Processing. These technologies automate manual tasks, allowing professionals to focus on more strategic initiatives.
Algorithmic Trading and Investment
While the most prevalent AI use cases in finance include risk modeling, financial forecasting, and hedging strategies, the technology can also help automate processes, improve operational efficiency, and enhance customer service. Specifically, banks can employ AI-enabled solutions to automate processes like identity verification and anti-money laundering, provide personalized assistants and chatbots, and perform exposure modeling and portfolio analysis.
However, financial institutions must ensure that their AI for finance solutions comply with strict regulatory standards. Otherwise, they can face fines and risk having their AI operations suspended. This is particularly true as new AI models are developed and rolled out. An excellent place to start is by establishing goals for the technology, ensuring that there is a data strategy in place, and experimenting with scalable AI-enabled solutions.
Unsurprisingly, banks were enthusiastic early adopters of machine learning technology. For decades, they used it to improve risk management processes and deliver efficiency gains and revenue opportunities.
AI’s processing power allows it to analyze massive data sets a fraction of the time a human would. Its cognitive computing capabilities manage structured and unstructured data, which humans cannot easily handle.
Then, the technology can perform risk-based modeling, which helps financial analysts identify risks and trends in complex data. This makes it easier for finance professionals to make strategic decisions and increase profitability. It also exposes latent risks and anticipates the future more accurately. In addition, it can automate back-office tasks and improve customer service. All in all, it’s an essential tool for the industry.
Data-Driven Decision Making
The quality of data used for AI can significantly affect its prediction power. Any financial business with an artificial intelligence unit must have a strict data-quality program.
AI can help banks monitor customer behavior for early signs of fraud, reducing the risk of losses and enabling them to detect patterns humans might miss. It also helps them analyze big data and identify trends in the marketplace to predict market fluctuations and assess risk.
Banks can use AI to automate front- and middle-office processes, saving them time and money. This could enable them to compete more effectively with their competitors. They may also reduce staffing and training costs. They could also use the technology to improve customer service and boost revenue.
Streamlining Regulatory Compliance
Artificial intelligence allows financial services firms to reduce manual errors in data processing and analysis, document processing and onboarding, and customer interactions through automation and algorithmic processes. This reduces operational costs by lowering the number of employees required for these tasks.
AI also helps with fraud detection by enabling faster and more accurate analysis of patterns to identify potentially suspicious transactions.
Financial institutions use proprietary and external data sources, including financial statements, historical market data, third-party databases, social media, news, geospatial/satellite imagery, and more, to improve the accuracy of their models. But, banks must ensure their AI systems don’t inadvertently discriminate against customers. This is possible by incorporating explainability in their AI algorithms.
Data Security and Privacy
Several financial services companies use AI to automate data entry, analyze and verify documents, process payments, and handle customer service. This frees employees to focus on high-value, complex tasks and reduces the risk of manual errors.
AI also improves the quality of risk assessments by enabling automated data analysis and modeling, allowing for faster and more accurate results. It can also help with credit decisions, quantitative trading, and risk-based management.
In terms of security, AI’s rapid growth and proliferation present several unique challenges. This includes data privacy, which requires a robust infrastructure. It’s also important to note that training generative AI models consumes more energy than 100 American homes. Therefore, constant improvements in security procedures are critical. This is especially true for finance, where data privacy and security are paramount.