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8 min read

How AI Has Taken Over the Financial Industry

AI/Artificial Intelligence/ML/Machine Learning/Automation/Model/Algorithm/Data/Analytics/Software/Engineers/Data Scientists/Automation/Frameworks/IoT/Algorithmic Modeling/Chatbots/Cybersecurity
How AI has taken over the Financial Industry. Banking and insurance are some of the best sectors for harnessing the potential of AI.
Written by
Anand Krishnan
Published on
April 12, 2024

Thinkbridge examines how AI has taken over the financial industry. Given the abundance of accurate data, a long-standing relationship with modeling and predictive analytics, and the quantitative nature of its services, the financial industry is one of the best sectors for harnessing the potential of AI.

By enabling banks, insurers, and capital market institutions to process large repositories of complex data with greater speed and accuracy, as well as to automate repetitive tasks ranging from servicing common customer requests to generating financial reports and performing reconciliations, AI technologies are transforming customer experiences and allowing institutions to reimagine their business models and processes.

The primary reasons that FS organizations use AI-powered solutions

Based on a survey of over 100 financial services executives conducted by the National Business Research Institute combined with research by Narrative Science.

[xyz-ihs snippet="primary-ai-reasons"]


Some of the key AI applications being used today include:

Chatbots/AI Assistants
On the front end, natural language processing and machine learning algorithms have enabled the creation of new customer service channels through virtual service agents using text and speech assistants or chatbots, mobile banking apps, and even VR. By automating common requests and queries, companies are able to provide customers with new technology-enabled experiences and the convenience of round-the-clock access to information and services, while streamlining staff workflows.

Targeted Pricing and Contracts
The computational capacity of AI also changes the game by enabling financial institutions to entice customers with more personal, relevant offerings. Advances in predictive modeling and machine learning software have made it easier for insurance and loan underwriters, for example, to better assess credit quality, and price and market insurance contracts and loans, providing more targeted offers and optimizing ROI. By automating many transactional processing tasks, underwriters can be freed to focus on higher-value activities such as helping to facilitate sales through relationship building and providing analysis through their nuanced understanding of unquantifiable factors affecting market conditions.

Robo-advisors and Automated Algorithmic Trading
AI is being used by hedge funds, broker-dealers, and other financial institutions to find profitable patterns and calibrate in real-time new trading rules to rapidly execute thousands or millions of trades a day. Meanwhile, automated portfolio managers, or robo-advisors, have also become increasingly popular especially with millennials who are more comfortable with the idea of ‘human-less investing. Companies such as BettermentWealthsimple, and French startup, Yomoni, automatically buy and sell stocks, bonds, and more, based on customer preferences and investment objectives, and many expect such AI-powered investment platforms to represent the future of stock trading.

Market Analysis
By feeding market data into algorithms, insurance, hedge fund, and financial institutions are able to detect trends and identify business opportunities e.g. are married men of a certain age group in a particular region less likely to get into accidents? What demographics are more likely to default on payments over a given time period or loan amount? Etc. Machine learning enables machines to build their own models or ‘learn’ based on historical data to conduct market analysis in real-time. In the future, AI technologies improve, these capabilities may even extend to encompass sentiment and news analysis.

Security and Regulatory Compliance
Security and compliance are two key areas in which AI and machine learning is playing a prominent role. As sensitive financial and personal information becomes increasingly easily accessible, and technologies evolve at an accelerated pace, AI is becoming an increasingly essential defense against the number and types of threats faced by organizations. Predictive analytics paired with machine learning can help institutions monitor data flows and operating environments, detect suspicious activities, and mitigate risks in a rapidly changing environment. For example, organizations are already able to identify fraudulent insurance/medical claims while they are being processed, or discover suspicious credit card activity as soon as they occur; and identify a person in the organization with irregular network or data activity. In contrast to a rule-based system of checks, AI-powered threat management is designed to adapt to changing risk environments and can help organizations allocate limited resources to make security decisions based on the highest probabilities to mitigate risks. Both public and private sector institutions may also use these technologies to better manage their data amid changing regulatory requirements.

Financial institutions are well aware of the disruptive potential of AI with many, if not all, aspects of their business processes. Nevertheless, there remain significant risks and challenges — both with the AI technology itself (e.g. the issue of false positives where AI is flagging activities that are not issued at all), as well as with the security, privacy, legality, and ethics of AI use (e.g. who owns the data? Who is responsible for the actions AI takes?). Many also express concerns over the unintended side effects of widespread automated trading and undesirable actions/decisions taken by AI algorithms.

Clearly, despite AI’s ability to assume much of the heavy number crunching and processing work, human input will still be essential.

PwC on selecting potential projects for AI in financial services

PwC’s Anand Rao, Ph.D. discusses how financial institutions should begin to select which artificial intelligence (AI) projects to invest in. Given the capital requirements for an undertaking, companies should look for those projects where AI can have the most impact.

Ultimately, the challenge for institutions will be to enable the machines to do what they do best i.e. to process large volumes of complex data, identify patterns and anomalies, and perform structured repetitive tasks, while making room for humans to do what they do best i.e. unstructured tasks, understanding the subtleties of human communication and culture, providing oversight and ensuring security, soundness and consumer protection of decisions, etc.

By retraining staff and restructuring business processes to enable symbiotic AI and human interactions, the result may create not only more jobs but also more interesting and higher value-added roles while facilitating enhanced, more efficient service, greater productivity, and improved ROI.

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