As you’ve seen from the previous articles in this series, Artificial Intelligence is a powerful technology debuting in various industries. Machine learning, a subset of AI, is the ability of a system to continuously improve its performance based on data collection and interpretation, embodying the “learning” aspect of artificial intelligence. This unique ability can play a significant role in the finance industry, as it is associated with investing, data collection and verification, budgeting, and consolidation. These manual tasks can be automated through machine learning, saving time and energy while managing finances, the pillar of practically every business.
Capabilities
Financial systems use Enterprise Resource Planning, or ERP, a set of applications that support their decision-making. The drawbacks of this system include requiring lengthy and time-consuming infrastructural upgrades. Recently, a shift to a “cloud” system approach through the Workday Enterprise Management Cloud with embedded AI has brought more ease to financial departments. Artificial intelligence and its subset of machine learning are the nuclei of this ease and efficiency.
Machine learning models solve specific problems and improve over time. They can quickly analyze large volumes of data to identify trends or forecast future growth and risks. For example, in the insurance branch, a customer’s data may be evaluated by the AI model that automatically determines their coverage and premiums. Some AI applications perform anomaly detection or predictive analytics, which examines past customer data to predict future outcomes. The speed and precision of these models compared to a human worker is unmatched. Automated decision-making ensures that the AI will perform mundane tasks such as verification and summarization at higher speeds and accuracies. On top of this, machine learning and the automation of repetitive tasks allow employees to focus on more intuitive ones, increasing the efficiency of the financial offices.
When it comes to customer service, companies can use AI applications that provide investing recommendations to customers after inspecting their history and financial goals. They can also minimize the necessity for in-person interaction by facilitating basic banking activities such as deposits, transfers, and payments. In the background, cybersecurity models can monitor purchase behaviors in a customer’s account and immediately report suspicious activity. Again, automated decision-making allows AI applications to provide customers with almost instant responses in their interaction, allowing for faster interactions, especially in the application processes of credit cards and loans.
Real-World Examples
Let’s cover some AI companies and their software that help financial institutions today. Ocrolus, based in New York, is a document processing software that utilizes machine learning to increase the speed and accuracy of financial document analysis. It has focuses, including mortgage lending, consumer lending, and business lending, that it uses to analyze financial documents. Socure, also based in New York, created an identity verification system known as ID+ Platform. It uses predictive data science and machine learning to examine a customer’s data and determine if their information is legit. Banks such as Capital One, Wells Fargo, and Chime use this application. In the investing branch, a platform named Entera allows real estate investors to purchase and market single-family homes and gain access to the properties on and off the market. The platform can use machine learning to discover market trends and match properties. Different AI companies are producing applications for many niches within this industry.
The capabilities of artificial intelligence in the financial industry are seemingly endless.
The Future
As the use of mobile banking continues to rise among US consumers, financial institutions will grow their digital banking models to accommodate. The speed and efficiency that result from artificial intelligence within their systems will help the growth of these institutions as they continue to digitalize and advance. Specifically, the 24/7 availability of AI services allows mundane tasks to occur in the background as more conceptual and intuitive tasks are handled by employees, making better use of their time at work and producing fruitful results. Machine learning provides these industries the hope of more efficiency, as its systems will continue to learn and develop automatically.
Works Cited
“29 Examples Of AI In Finance”. Built-In, 2023, https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies. Accessed 30 July 2023.
“AI In Finance: Applications, Examples & Benefits | Google Cloud”. Google Cloud, 2023, https://cloud.google.com/discover/finance-ai#section-1. Accessed 30 July 2023.
Intelligence, Insider. “Artificial Intelligence In Financial Services: Applications And Benefits Of AI In Finance”. Insider Intelligence, 2023, https://www.insiderintelligence.com/insights/ai-in-finance/. Accessed 30 July 2023.
“What Is AI in Finance? | Glossary.” Hpe.com, 2023, www.hpe.com/us/en/what-is/ai-in-finance.html. Accessed 30 July 2023.
“What Is AI In Finance?”. Oracle.Com, 2023, https://www.oracle.com/erp/ai-financials/what-is-ai-in-finance/. Accessed 30 July 2023.
“What Is AI in Finance? Your FAQs | Workday.” Www.workday.com, www.workday.com/en-us/pages/what-is-ai-in-finance.html. Accessed 30 July 2023.