Machine learning will transform the banking sector

By Robin Trehan

Machine learning is a prerequisite feature of an intelligent system that supports the banking sector. Without it, there is no possibility of enabling data-driven predictions that can generate new business opportunities for individual institutions. The entire industry is already taking advantage of this technology so that it can improve products and customize services for all consumers. 

Artificial intelligence is the technological investment used for the banking industry to have access to machine learning. It is the branch of AI that enables machines to begin learning how to perform or complete specific tasks by themselves. It is already in use through the inclusion of automatic reply predictions of email messages, virtual assistants that interact with customers who require help, and facial recognition systems that can prioritize individual data before the consumer approaches an individual at the institution for assistance.

Machine learning provides the banking sector with the ability to convert data samples into new interferences based on how the technology interprets the information it receives. Banks are using old mathematical challenges to create new computing opportunities with their investments in this arena.

Machine Learning Uses Three Structures to Produce Improvements

Artificial intelligence and machine learning have seen significant improvements since the technologies started becoming widely available at the turn of the 21st century. The primary reason it has seen high-level development during this time was because of the opportunities developers provided that allowed for self-learning opportunities.

The processes of machine learning allow artificial intelligence constructs to continually train against itself so that it can improve its overall interactions. This structure works to reinforcing how the technology learns so that it can be an efficient model for the banking sector.

Three structures are useful for the development of artificial intelligence through machine learning. Each one offers specific advantages that help the banking sector develop new efficiencies, reduce overhead, and increase consumer customization simultaneously.


1. Reinforcement Learning

This activity occurs when AI learns something new through a trial-and-error process until it discovers the most efficient way to complete a specific task. Machine learning allows the system to acquire knowledge by modifying its behavior through the completion of rewards for completing assigned tasks without receiving specific programming to perform in a particular manner.

2. Supervised Learning

This structure occurs when artificial intelligence technologies get trained through the use of labeled data. When a photograph appears with a specific description of the items that the image contains, then the algorithm that the machine learning process uses can select this information from the other databases to which it has access. This approach allows the AI to discover unique details for particular customers automatically, increasing the opportunities to provide customized industry products.

3. Unsupervised Learning

This opportunity gives machine learning technologies a chance to look for similarities in labeled databases instead of attempting to identify unique patterns. Programs provide algorithms that are not programmed to detect particular data types, such as a specific account, but to look for examples from individual consumers have are similar and could get grouped. By standardizing this research process, it becomes possible for a bank to identify new service opportunities that would typically get missed if only humans looked at the information provided by the customers.

The flexibility that machine learning offers allows this technology to adapt to data alterations as they happen in the system. That structure enables AI to learn from its actions, creating momentum that leads the industry to a future where automated customization becomes a possibility.

Machine Learning Provides an Omni-Faceted View of Consumer Footprints

Machine learning generates an enormous opportunity for the banking sector to understand the entire consumer footprint. The unified approach that AI provides allows institutions to see the financial status of a customer across multiple accounts in an instant. This information combines with the previous products that got contracted, transaction histories, and individual interactions to ensure that personalized services are always available.

This technology redefines the definition of customization. Instead of requiring a customer to complete a series of questionnaires or surveys to match up specific datapoints to potential products of interest, machine learning automates this process by looking at all of the activities of the consumer throughout that person’s history with the organization. It can even pull information from the news or social media posts to determine the viability of an offer before one gets requested. That makes the predictive mechanisms more accurate, speeding up the time it takes for someone to complete the processes needed to access something new.

As improvements to data warehousing and information processing continue developing, the existing machine learning models will use specific financial profiles that the technology develops internally to create unique initiatives that encourage increases in customer interactions. According to Deltec Bank, Bahamas– “Banks will then use the information from these processes to add more value to the products and services offered each day. Consumers can also benefit from AI in this manner because the technology can deliver relevant financial opportunities or advice without requiring a prolonged conversation with a human representative. “

Machine Learning Reduces Risk Levels for the Banking Sector

When machine learning can increase productivity and improve customer service, then it can also reduce the uncertainties of individual transactions. That data can help banks evaluate risk factors for individual consumers with greater accuracy because the information is based on a broader digital footprint.

AI frees up essential staff from completing the most basic activities found in the banking sector so that they can focus their time on the considerations that add the most value to the individual consumer. By targeting the simplest recurring processes in daily duties, institutions can facilitate more opportunities for self-service where consumers can use the data they generate to make empowered decisions for themselves.

That means the banking sector can reduce the instances of fraud by using machine learning to identify uncharacteristic behaviors on individual accounts. Algorithm models have reduced the number of false positives by 54% in recent years through Deep Feature Synthesis, extracting over 200 specific attributes from each transaction.

When this approach from machine learning gets applied to other areas of the banking sector, each interaction will provide accurate opportunities to manage accounts or find new investments. It streamlines the approaches used to interact individually while increasing the amount of customization that is available to everyone.


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