Automation in Lending

In recent times, the banking world has gone through a series of transformations and has been re-defined by technology. Digitization, digitalization, and data analytics have changed the face of banking worldwide. However, technology can be both a challenger and an enabler and the question is how is the banking system adopting and adapting to this change? To what extent are banks leveraging technologies like machine learning, Artificial Intelligence, and big data to get a competitive advantage?

Or is Fintech now a hygiene requirement for financial institutions?

The banking industry was the first one to adopt automation in fraud analytics, credit underwriting, and customer personalization. Today, nearly50% of the personal loan portfolio is managed through digital technologies. In the backend, there is a machine learning algorithm that is underwriting loans to speed up the processing, doing fraud analytics as well as making offers to customers in real-time.

In banking and securities, robotic process automation (“RPA”) is already widely used to automate workflow and decision-making for a variety of rules-based core processes, such as loan origination and collections. As the technologies mature, many firms are expanding their use of automation and cognitive intelligence (“CI”) to drive efficiency, effectiveness, and productivity throughout the enterprise.

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Automation Potential in Banking and Finance
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  • According to a recent report published by Fortune Business Insights, the globalrobotic process automation market size is projected to reach USD 6.81 billion by the end of 2026. Leading analysts also estimate a dramatic increase in the market size of RPA technology.
  • As per Gartner, the market size for RPA solutions is estimated to reach $2.4 billion by the year 2022.
  • As per Forrester’s RPA trends and forecasts, the market for robots in knowledge-work processes will reach $2.9 billion by 2021.

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Therefore, the change is real and is happening at lightningspeed. But it is not just the back-end that is changing. Firms are now seeing technology as a USP to win over consumers. Convenience isking and time is money. And Fintech allows banks to master both.


Banks are benefitting from the use of machine learning not just in terms of quick operational decision-making but also in improving customer engagement.With the use of AI, banks can transform the entire customer journey.Embedding dataas a part of thecustomer journey and using the right data sources andelements of machine learning or AIprovides multiple benefits like-

  • Portfolio Management
  • Easy audit and compliance
  • Easy accessibility of information and documents
  • Smooth integration across platforms
  • Security compliance
  • Enhancedcustomer experience

A360-degree transformation is being noticed in the banking, lending domain.Studying customer behaviorand transactional patterns is helping in understanding loan requirements better and in taking smarter lending decisions. Banks can seek information onthe transactional flow from internet banking and decide proactively whether to make that lending available to SME clientsor not.

In United States, it takes approx. 50 to 53 days to process mortgage loan. Process of approving mortgage loan goes through various checks like credit checks, repayment history, employment verification and inspection. A minor error can slow down the process. As the process is based on specific set of rules and check, RPA can accelerate the process and clear the bottleneck to reduce the processing time to minutes from days.
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According to Bain & Company, determining the best way to integrate smart automation technologies such as RPA and AI in a way that effectively promotes customer loyalty, operational success, and employee satisfaction is a balancing act. A mature solution will address the distribution and combination of attended and unattended processes, as well as RPA systems augmented by AI tools.
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The amount of data being generated from day-to-daybanking operationsby millions of customers and the already available historical data iscollectively beingutilized to create new offerings. This isachieved through arrangement, management and ingestion ofthisdata to a commonsystemwhere it isstored and thoroughly analyzed. This then helps in building next generation data platforms to scale up the analytics insight based on the organized data.

In this digital era, speed is a very important element. Accurate data analytics with supervised machine learning will help banks reduce cost of operation and help in upsellingand cross sellingof products significantly. The keyis to get the data right andscale up withAI capability.It is not only about the quantum of data; it is also about thequality of data and itsaccessibility. The focus is to improve the quality of legacy data and leverage information available through public utilities to take decisions. Banks need to leverage language and voice bots to service customers along with keeping in mind the security considerations as hybrid cloud is being used to manage and access data.


Activities like advanced threat hunting exercise,increase the costs involved in automation. However, this can easily be compensated when the cost involved in building and maintaining technology are monetizedwith optimum utilization of data. There is littleuse of advanced infrastructure and security system if value added benefits are not observed in the long run. At the end of the day,how efficiently the dataisbeing used is of utmost importance. We need to leverage the data usage in such a fashion that it will be advantageous for the organization as well as the customers. Automation helps in eliminating redundancy and allows for efficient utilization of the workforce which in turn leads to both cost and time savings.

Research shows that implementing RPA drives about 25% to 50% cost savings, improving the output metrics of applied functions.
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To makecustomers believeinbanksastheir safefinancial partners, financial institutionsneed to invest in internal process improvement, new technological solution to prevent crime and also to educatecustomers. Wheneverprocesses are fully digitized, and modern technologylike AI and machine learning areused along with appropriate dataaccuracy,operational risk will automatically be reduced. Moreover,AI can help scan abnormal behavior of users and identify a pattern, which also reduces organizational risk.

However,investment in technology alone will not make a safer financial partnership with customers. Investment in peopleand culture is equally importantin providing customers a safer platform. With increased number of customers,there is an increase in thedigital footprint being left behind and itcan be leveraged to build a robust and more efficient system.

Advanced data and analytics with robust cyber security to maintain operational riskisthe key tobuilding a cognitive enterprise. With optimum utilization of Artificial Intelligence and Machine Learning, automation can be scaled up throughout variousprocesses, while reducing costs, ensuring adherence to industry standards and norms and tying everything together to improve delivery services and customer engagement.

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