Impact of AI on the Financial Services Industry
Over the past few years, Artificial Intelligence has made significant strides, applying itself to nearly all sectors of the economy, from Healthcare to Logistics. The use of Machine Learning (ML) provides a wealth of applications that are improving production and efficiency in numerous ways, especially in the financial industry.
The application of AI in the financial sector goes beyond optimizing investment strategies too. Advanced Deep Learning (DL) algorithms can be used to help detect fraud, automate transaction routines, and more. There are a number of ways that AI is improving finance, but more improvement is still needed.
The Current State of AI in the Financial Services Industry
Many of the world’s largest financial institutions are leveraging AI in their everyday operations on both the front end and back end of their services:
Fraud Detection: AI is used to detect suspicious transactions to protect account owners. The AI can analyze spending habits and locations to identify fraudulent activity that can occur online.
Risk Management: Companies can assess better risk management strategies using AI models. They can also help with credit scoring and insurance underwriting.
Customer Service: Chatbots and virtual assistants use Natural Language Processing to help customers with queries and concerns, reducing overhead costs and operating 24/7.
Trading: Algorithm trading allows users to make high-frequency trades with more accurate predictions and executions, helping buyers and sellers get the best deals.
Advisors: Automated advisors can be used to manage portfolios with minimal human intervention, simplifying investment activity based on a user’s goals and risk tolerance.
Benefits of AI in the Financial Services Industry
Deep Learning brings many benefits to the financial industry, especially its ability to automate tasks. Responsibilities like data analysis and transaction processing can become resource intensive. By shifting these routines to AI models, humans can focus on higher-value tasks using the processed information.
Not only can deep learning sift through raw data faster than humans, but it can also learn from the data it analyzes, allowing it to provide more accurate results over time. These continuous improvements can bolster operational efficiency for processes like document verification and loan origination.
For example, a customer may want a new credit card, so an algorithm analyzes their transaction history to determine their credit risk. It can then be used to tailor the optimal rates and limits for that specific customer to both their benefit and the financial institution.
Challenges and Potential Risks
Despite the vast amount of application AI has in the financial industry, many ethical concerns need to be solved before progress can continue. The financial industry has a powerful ripple effect on our global economy, so AI’s use must be as transparent as possible.
In deep learning, many algorithms do not provide the reasonings for their decision-making, creating a “block box” that requires Explainable AI to make sense of. When operating in an industry that affects the financial aspects of everyone’s lives, transparency needs to be present.
However, transparency can go too far as well when it comes to data security and privacy issues. While algorithms are capable of crawling through transaction histories, financial information is highly sensitive and having it stored within a neural network server could pose major problems in the event of a security breach.
Likewise, customers may also have some concerns about their entire financial history being uploaded and processed by AI, potentially having it used to lock them out of certain financial products and services.
Future Prospects of AI in the Financial Services Industry
As concerns over AI’s use in the financial industry continues, many are starting to call for regulations on the technology to limit its exposure to private/sensitive information. However, there are still many exciting prospects for the future of technology in finance:
Predictive Analytics: With large supplies of data, AI can improve its ability to predict future trends and behaviors, helping to forecast markets and consumer habits.
Enhanced Personalization: Financial service providers can offer more personalized services for custom investment portfolios.
Blockchain: Blockchains contain a massive amount of transparent data that can be fed to DL models to help execute smart contracts.
Quantum Computing: While still far from being fully developed, quantum computing can use AI models to assist with risk modeling and high-frequency trading.
The use of AI in the financial industry poses a lot of potential by removing much of the mundane, allowing employees to focus on more important matters like building strong relations with clients or focusing on more important matters of production.
