Ai is increasingly being implemented and proving its worth in the financial services sector.
With the recovery from the COVID-19 pandemic, financial services institutions are now moving from experimentation to implementation with artificial intelligence (AI) and machine learning (ML). We increasingly see the large-scale adoption of these technologies. The pandemic compelled financial organizations to adopt technologies that will help respond to customer needs around the clock. The effect has been widespread transformation at such as rapid pace to ensure critical operations run without interruptions.
More companies in the financial services sector have developed a keen interest in AI and ML-backed solutions. The advantages are reducing the need for manual interventions in their operations, improved security, and freeing up resources and time for continued innovation. AI and ML technologies have promised long-term value and strategic advantages besides significantly reducing the time between generating ideas and delivering value for businesses. Traditional financial institutions such as banks are now transforming into digitally-driven businesses by developing the capacity to a relentless focus on their customers, just like the big tech firms.
What are critical use cases of AI and ML that banks and financial institutions can benefit from the following implementation?
A good number of financial services enterprises had already begun experimenting or adopting AI and ML before the pandemic. The challenge back then was identifying the essential functions that would benefit immensely from AI. In several instances, the adoption of AI technology didn’t deliver the expected returns. Following the pandemic’s start, the financial services sector was compelled to fast track AI and ML technology deployment for seamless service provision. AI will be at the heart of recovery efforts post-COVID-19 since it has been vividly apparent where the technology will be most required and impactful. Some of these areas include fraud prevention, arriving at credit decisions, and improving customer experience through 24/7 interactions.
The following areas can be improved through the adoption of AI and ML in the financial services industry:
Document Processing with Intelligent Automation
AI and ML technologies enable intelligent and robotic automation, helping optimize different functions, improve speed and accuracy of financial processes, enhance overall efficiency, and lead to significant cost savings. For example, blockchain has had a prominent deployment in e-KYC (electronic know your customer). The e-KYC is paperless and done remotely, which helps reduce the otherwise bureaucratic costs of protocols used in KYC, such as verification of client signatures and identities. Tasks and repetitive and mundane processes such as keeping track of document handling, regulatory reporting, and loan disbursement and repayment are gradually being automated. Financial organizations use intelligent automation platforms to extract, manage and interpret unstructured data that includes scanned documents, text, images, faxes, and web content. The natural language processing (NLP) engine characterized by high levels of accuracy and reliability helps identify unseen, missing, and ill-formed data. As a result, average handling time is effectively reduced, which gives organizations a competitive advantage by providing a much-improved customer experience. A new startup highly evolved in this sector is LutinX, with innovative solutions already active in different countries.
Thorough and Efficient Customer Support
The use of virtual assistants helps financial organizations respond to customer needs with minimal employee input. Chatbots have become widely used on e-commerce sites, and similar solutions are expected to become popular in the financial services sector. Some organizations such as JP Morgan are using the bots to facilitate streamlining back-office operations and offer customer support. The net effect of deploying chatbots and virtual assistants has increased productivity, reduced time and effort that goes into generic customer queries, and freeing up teams for critical and long-term projects that will drive innovation. All the platforms used utilize contract intelligence, abbreviated COIN, that relies on machine learning systems. COIN automates legal filing tasks, reviews documents, and handles basic IT requests such as password resets. Other uses of the technology are creating new tools for clients and bankers to help reduce human error and provide more excellent proficiency.
Risk Management Analytics
Creditworthiness is primarily estimated based on the likelihood of an individual or a business to repay a loan. Risk management processes in all lending institutions are based on determining the chances of default in loan repayments. There are challenges associated with this process, even with impeccable data, since some individuals and organizations will not be truthful in articulating their ability to pay back a loan.
Financial companies such as ZestFinance and Lenddo have deployed AI to determine creditworthiness and risk assessment. Other examples include credit bureaus such as Equifax that are using AI and ML and advanced data tools to analyze alternate data sources to offer customer insights and evaluate risk. The use of AI has allowed lending organizations to expand the limited data set for this process. AI has helped analyze an individual’s entire digital financial footprint to determine their creditworthiness and default risk rather than the traditional use of credit scores and annual salaries. The alternate data analysis helps determine creditworthiness even in the absence f conventional loan records and credit history.
Adoption is Happening Now
Ever since the pandemic started, the way businesses and their clients interact changed irreversibly. It hasn’t been any different for the financial industry, which was already experimenting and deploying AI and ML on a limited capacity. The urgency brought about by the pandemic had led to the widespread adoption of AI to spur innovation and increase resilience in the financial services sector.
Financial institutions are well aware of the critical areas in which they can benefit most through AI. The transformation in its infancy before the pandemic has accelerated remarkably to almost becoming the standard approach. Adopting AI is evident in back-office operations and customer engagement bringing about improvements in efficiency and response times. The full implementation of AI is bound to bring immense benefits in the future.
Author: Alessandro Civati