How Does Machine Learning Help In Finance?

Machine learning in finance belongs to artificial intelligence (AI) and has become popular in recent times. It enables computing algorithms to have large amounts of data and affordable computing work.

It enables accurate computer predictions when new data is exposed or presented. Data scientists develop such models and machine learning guides by training them on existing or newly developed data links.

Artificial intelligence in finance is a technology that is paying higher dividends today. Machine learning is combined with various processes in the financial market to organize, collect, implement and interpret large amounts of data.

In addition, it allows you to learn from changes to adapt financial services in a responsive, efficient, and timely manner. Artificial intelligence offers banks and the financial industry a way to meet customers’ demands who want a more convenient, smart, and secure way to spend, access, invest and save their money.

Below you can have the best information on this topic and how machine learning transforms the financial industry.

Why is machine learning right for the finance industry?

Machine learning in finance is revolutionizing and transforming the banking or financial services industry. The world’s leading financial companies and banks implement artificial intelligence technology such as machine learning (ML).

This allows them to optimize portfolios, streamline their processes, underwrite loans, and reduce risk.

Machine learning is about understanding and digesting large amounts of data and learning from that data to accomplish a specific task. It allows you to distinguish authentic documents from fraudulent legal documents.

Machine learning in the finance industry consists of using various techniques to safely and intelligently handle large amounts of information.

Machine learning in finance is important to finance companies for the following reasons:

  • By automating processes, you can have a reduction in operating costs.
  • You will be more productive, and user experiences will improve, which will bring you more revenue.
  • Enhanced security and better compliance.

Due to the amount of historical financial data that the financial industry generates, machine learning has been able to find a wide variety of useful applications. Useful finance apps include:

  • Algorithmic Trading
  • Portfolio Management – Robo-Advisors
  • High-Frequency Trading (HFT)
  • Chatbots
  • Loan / Insurance Underwriting
  • Risk Management

Benefits of Machine Learning in the Financial Industry

The benefits of machine learning (ML) in finance are fundamentally focused on working with large volumes of data quickly, safely, and without errors. Benefits include:

  1. Work with big data

As mentioned above, the financial industry struggles to gain an edge by using big data. They can make easy and accurate predictions for financial processes in credit, transactions, loans, security, banking, and process optimization.

  1. A well-created and established data structure

There are well-documented APIs in the financial market, and the established data infrastructure provides machine learning professionals and data scientists with a large number of markets in real-time. This allows them to apply machine learning and modeling techniques.

  1. Reduce human error

During the 1950s and 1960s, human error in the financial industry was a big problem. Paperwork and analog instruments have now been replaced by automated and computerized systems that reduce errors caused by finance personnel.

Effective ML models used on large amounts of data help achieve lower error rates than workers performing the same tasks.

  1. Reduction of workloads

Machine learning works perfectly for high-volume or repetitive jobs, such as cleaning and formatting data sets. It allows making millions of forecasts and predictions simply in a short period.

Companies that run on machine learning are more secure and efficient, have lower operating costs. On the other hand, human resources may be directed to areas of the company where they can add greater productivity and value. These areas can be:

  • Customer-facing roles
  • Management
  • Business strategy
  • Creative tasks


  1. Creating value with greater predictive power

Machine learning models can help create greater value for banking, the financial industry, and their clients, such as:

  • Banks quickly predict which transactions are fraudulent and which are legal transactions.
  • Investment portfolios can react quickly to market forces to increase their ROI.
  • Loan companies can predict what can and cannot repay your loans. This is beneficial because it allows you to loan money only to customers who can pay it back.
  • Lenders will be able to more precisely tailor financial products to clients with recommendation systems through the Surprise library.


  1. Transparency and without bias

Machine learning in finance gives you greater transparency. Judgments made by machine learning algorithms are more likely to be more transparent than human judgments. This will depend on the following aspects:

  • How unbiased or biased is the training data.
  • If the training data is real and represents a truly representative sample of the existing population.
  • The size of the training data (mainly the more, the better).
  • If a model data leak occurred during training or the training functions represent an environmental context.

How is artificial intelligence used in finance and the companies that lead it?

  • DataRobot

DataRobot help companies and financial institutions quickly create predictive and accurate models to improve decision-making. They help with fraudulent credit card transactions, direct marketing, digital wealth management, blockchain, loans, and much more.

Businesses using DataRobot software will make smarter and more accurate decisions by predicting which customers are most likely to default.

  • Scienaptic Systems

Scienaptic Systems is a company with more than 100 million customers. It uses artificial intelligence to connect multiple structured and unstructured data, learns from every interaction, intelligently transforms data, and delivers contextual contract intelligence.

They have a huge impact on the industry because they work with a major credit card company. The company has commented that it could save $ 151 million in losses in just three weeks.

  • Underwriter.AI

This platform acquires portfolio data and applies machine learning to find different patterns to determine which applications are good and bad. The company indicates that it can reduce absences and defaults between 25 and 50%.

There are online lending companies that have worked with the Underwriter.AI platform and have reduced their default rate from 17% to 5.4%.

Machine learning in finance is an important model for financial companies that want to improve their work and service.

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