Machine learning in finance is one of the biggest buzzwords in the finance sector for so long, but no more. Over the past decade, technological progress caught up with real life, with data science revolutionizing the entire industry. The digitalization of investment and money matters can give you all the edge you need.

Finance companies that have no way to leverage machine learning are being left behind. By combining machine learning with many elements of big data, companies can take advantage of financial opportunities that almost nobody takes.

Here are 6 ways machine learning is revolutionizing financial companies today. These fintech trends are changing the landscape of the finance industry as we speak.

1.  Process Control and Optimization

Process control and optimization are sets of regulations that maintain or optimize workflow without stoppage or deviation from expected results. In finance, every process needs to bring in expected results without fail. With PCO, maximizing reducing bottlenecks can help improve process efficiency.

With machine learning, companies are slowly pushing out manual work, with some organizations even removing it from their entire workflow. An application like an expense management solution can root out congestion within the company. Fewer bottlenecks can result in exponential increases in overall efficiency.

Organizations now use cutting-edge technology like AI to do jobs that entire departments do in more time. Business processes become faster, more efficient, and more accurate. PCO also refines employee training and speeds up any accounting activities that exist in the company.

The current challenge of AI-driven PCO tech is the complexity of technology. Soon, technology will be more widely available and drive businesses towards more automation opportunities. Shorter processes will result in a higher return on investment (ROI) down the line.

2.  Loan and Insurance Underwriting

Loan and insurance underwriting requires the analysis data to enact several crucial processes, which include risk assessment and checking for qualifications. If you’re a payday lending business that needs to calculate if an applicant qualifies for your loan, you want to simplify the entire process to close the account in a short time.

Machine learning allows lenders and insurance companies to automate tasks like underwriting and credit checks in a fraction of the time. Matching data records, finding exceptions, rooting out potential history, and more processes can happen faster than what entire teams and departments can do.

Data scientists can help train the company AI to recognize trends and patterns with both viable and risky clientele. Companies who leverage machine learning in their loans and insurance can use hundreds and even thousands of data points to assess who has a history of risk and default.

3.  Financial Trends and Analytics

Predictive and prescriptive analytics are two types of powerful machine learning capabilities that help give banking institutions the edge they need. Predictive analytics uses data to understand future trends and calculate potential changes, while prescriptive analytics offers possible decisions and what can be done with the data.

Leading banks use both capabilities to give their customers a more personalized experience with their products. With both analytics, companies can recommend products and services that suit the needs of their clientele. It will also help their bots in processing transactions that don’t need specialized knowledge or human intervention.

Predictive analytics also helps financial institutions to predict customer spending habits and behavior. By removing the guesswork from the entire process, businesses can define a variety of experiences defined by user segments. They can tailor unique experiences and provide individual recommendations that can help make their services more desirable.

4.  Portfolio Management

Since the inception of the financial industry, human portfolio managers act upon many stock and investment processes. From exchange-traded funds, hedge funds, and mutual funds, clients need to rely on them in exchange for a certain value from the returns. This is where machine learning comes to play.

With AI and machine learning, robo-managers can save clients a hefty chunk of their money for the same, if not better, returns. These advisors use thousands of data points to help clients understand uptrends and risks while helping fine-tune your assets.

With an AI manager, you can find the level of risk that you’re comfortable with and find high-value investments that many would think are “diamonds in the rough”. Robotic portfolio managers use historical data and descriptive analytics to find the highest-performing investment allocations you can do.

There is little to no human input with AI portfolio managers, driven by massive data points and learning algorithms. You’re not paying someone’s salary, so you’re less likely to be charged more. AI managers will also do menial tasks like generate reports, perform portfolio audits, and more.

5.  Fraud Detection

Fraud detection is among the sectors of the financial industry that benefits the most from the newest trends in fintech. Artificial intelligence and data science are no exceptions, with companies using machine learning to find suspicious activities within their business.

Real-time alerts, or those close to it, are invaluable in finding hidden and abnormal operations. These can save the company a lot of time and money, especially by allowing fraud analysis to stay in-house. Machine learning programs also offer more than keeping out hackers and unscrupulous players.

Machine learning applications can help in error reporting and correct potential issues like double entries. AI can flag errors in numbers and trends, which can cost companies upwards of millions of dollars. They can also explore solutions and analyze documentation that can help seal off potential breaches within the workflow.

6.  Image Recognition and OCR

As we noted, process control and optimization is a powerful way to leverage machine learning capability. Among the most distinct capabilities that deep learning use are image recognition and optical character recognition (OCR). These are invaluable for document analysis within the financial industry due to their sheer speed and accuracy.

With image recognition and OCR, companies that leverage machine learning can handle documentation, that takes humans days to process, in a matter of seconds. Most systems that perform image recognition help interpret documents, automating the entire process.

Big enterprises that use such technologies invest a lot of money to train their ML applications to scan systems and analyze legal documents at high speeds. By doing so, they save tens and thousands of manhours, as well as millions in salaries.

The Bottom Line

Machine learning is not simply a trend in fintech anymore. It is here to help the financial industry evolve to the next level. As technologies develop, AI and machine learning systems can do many things for your business.

In the years to come, it will be essential for you to leverage these types of technological solutions if you want to get ahead of the curve. What else can machine learning do to revolutionize finance companies? Only time can tell.