NBFC

How Do You Leverage Data Analytics for Your NBFC In India?

Due to rising credit and financial needs, India’s Non-Banking Financial Companies (NBFC) sector has witnessed tremendous growth, especially in the last few years. Advancements in technology and the accessibility of enormous data sets have made NBFCs the right fit to address data analytics to improve operations, fulfil customers’ needs, develop strategies, and make informed decisions. Therefore, through the knowledge of data analytics, NBFCs can have an edge in the newly changing world financial scenario and get the best out of these changes.

Historical Context of NBFCs in India

Let’s discuss the historical context of NBFCs in India to understand more about the NBFC sector:

Business Intelligence (BI) Tools

Those tools assist developers in creating dashboards, graphs, charts, and reports intended to present gathered and analyzed data like Tableau, Power BI, and QlikView living in rural and semi-urban areas. The main goal was to support underserved regions of the population without access to traditional banks to procure financial services to indulge in work and build ventures.

The Reserve Bank of India (RBI) has been the main regulating agent for the NBFC sector, ensuring that the institutions operate under guidelines that foster stability and safeguard consumers’ interests. The RBI Act was passed in 1934. NBFCs formed a solid foundation in the 1970s, and consumer NBFC laws were set up in the 1990s, leading to significant growth in this sector.

The Narasimham Committee Report 1997 recommended efficient and full-fledged regulation of NBFCs, including transforming their functioning into a structured standard of operations. This sort of regulation has been essential in the industry as it ushers in the stability of the overall financial market and protects the consumer.

The use of technology, especially after the liberalization of the Indian economy in the 1990s, has significantly affected the NBFC. The growth of digital channels, mobile banking, and Fintech solutions has helped NBFCs expand their customer base and launch new value-added products. Thus, this technological shift has laid down a platform for data analysis to become the central part of NBFCs’ operation models.

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Let’s Discuss the Functions of NBFCs

Data analytics can be defined as the process of using computational tools to analyze available data to make decisions. For NBFCs, leveraging data analytics can lead to:

Enhanced Customer Insights

Data analysis can identify customer behaviour and decision-making for better service provision in NBFCs. NBFCs can then segment their customer base by analyzing transaction, feedback, and demographic data and creating specific, streamlined marketing plans.

Risk Management

Finding possible hazards connected to lending requires the use of data analytics. Based on historical data, NBFCs can evaluate borrowers’ ability to repay the loan, estimate possible defaults, and control the risk involved. Due to the availability of advanced analytics techniques, including predictive modelling, NBFCs can make the right credit decisions and minimize non-performing assets.

Operational Efficiency

NBFCs can track various operations to find out the areas or methods that are ineffective in terms of operations and streamline them effectively. Evidence indicates that data analytics can assist in cost-cutting, minimizing processing times, and improving service delivery. This results in cost savings and enhancement of streamlined profit margins.

Regulatory Compliance

Reporting and monitoring help regulate compliance since data analytics automates many tasks. For example, NBFCs can use analytics to monitor compliance statistics, pinpoint possible violations, and create reports that will be beneficial for the regulating authorities to avoid penalties and reputational losses.

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Current Trends in Data Analytics for NBFCs

The COVID-19 pandemic has made this shift even more difficult by forcing most firms to operate online. NBFCs are enhancing their use of technology to increase customer interaction and ensure a streamlined flow of operations. Customers can quickly obtain financial services through digital channels like web portals and smartphone apps, which also give NBFCs valuable data for research.

Use of New Age Technology

These applications have used technologies such as machine learning (ML) and artificial intelligence (AI) to evaluate an individual’s ability to repay a loan and the likely customer behaviour. These technologies help NBFCs develop better decision-making structures, risk assessment evaluation, and customer relations.

For example, ‘smart’ chatbots can help customers get instant solutions to their problems, and ML algorithms can help identify complex patterns and trends in a huge amount of data.

Making Future Predictions

It is used to predict future scenarios based on past occurrences, which is helpful for NBFCs. Thus, NBFCs can predict the events of customer defaults, market variation, and demand for financial products and thereby control risks. For instance, through statistical models, NBFCs can recognize customers who may fail to repay the loan and take appropriate measures. action

Tailored Solutions

Data analytics help NBFCs offer tailor-made financial products and services, resulting in client satisfaction and the ability of NBFCs to retain and maintain goodwill with their clients. Segmentation lets NBFCs organize their clients based on their behaviour and preferences, supporting the implementation of focused strategies. Customized marketing initiatives can potentially increase customer satisfaction and improve conversion rates.

As new rules emerge, NBFCs are not using data analysis to meet the new regulation standards. For example, Regtech solutions help automate compliance and thus burden the firms.  This is because, through analytics, NBFCs can conduct transactions for any aspect of illegality, such as AML and KYC compliance.

Steps to Leverage Data Analytics

NBFCs should have a clear vision of their objectives before using data analytics. Whether the aim is to achieve customer experience, risk management, or operational efficiency, having a clear focus will help steer the analytics approach. Having measurable goals will make it easier to determine the impact of the analytics initiatives.

Data Collection

Analytics requires huge data sets. NBFCs must incorporate strong data collection processes to collect relevant data such as customer transactional records, feedback, and market trends. Data can be collected through:

• Transactional Data

Any details recorded regarding customers’ activities, including loan requests, payments made, and other events performed on accounts.

• Customer Feedback

Questionnaires, feedback, and research reviews approximate customer satisfaction levels and needs.

• Market Data

Competitive analysis, market trends, and economic indicators all influence strategic decision-making.

Data Management

Sensitive information conservation is essential, and this can only be given a solid foundation when good data management practices are embraced. NBFCs should eliminate the lack of protocols or policies relating to data management and embrace data management policies and guidelines that set out how data is acquired, processed, shared, and protected. Key components of data management include:

• Data Quality

This involves completeness, accuracy, and consistency of data collected and stored in the organization.

• Data Security

They report customer details leakage and incorporate measures to protect against such losses.

• Data Integration

Creating a single view for analysis by combining data from multiple sources.

Using Analytical Tools

Much involves putting capital into the right analytical tools and technologies. NBFCs can leverage a variety of software solutions, including:

• Business Intelligence (BI) Tools

Those tools, such as Tableau, Power BI, and QlikView, assist developers in creating dashboards, graphs, charts, and reports intended to present gathered and analyzed data.

• Machine Learning Frameworks

Machine learning tools for building reliable and efficient predictive models, such as TensorFlow and Scikit-learn.

• No-code Platforms

Tools that enable professionals to develop analytics applications with minimal coding skills, expanding the use of data analytics within the organization.

Data Analysis

One of the most important steps includes the analysis of data that is collected and managed.

NBFCs can use various analytical techniques, including:

• Descriptive Analytics

Using historical records and data to evaluate the performance and trends of the company in the past

• Diagnostic Analytics

Finding out what led to past results and what occurred in them.

• Predictive Analytics

The process of using historical trends and patterns to forecast future events or human behaviour.

• Prescriptive Analytics

Offering suggestions or possibilities for improvement on an issue of interest on an organizational level based on the data collected.

Developing Strategies

The last step is applying data analysis insights to business operations strategies. This may lead to changes in lending procedures, improvement of customer services, and other organizational procedures. Evaluating the measures implemented is also crucial since it helps determine the organization’s efficiency.

Challenges in Implementing Data Analytics

While the benefits of data analytics are significant, NBFCs may face several challenges in its implementation:

· Data Quality

To get the best possible analytics outcomes, it is essential to ensure the data is sourced from a trusted source, relevant, and up to date. Poor-quality data can lead to Inaccurate insights and decisions. NBFCS must have sound data validation systems and a cleaning process to have high-quality data available.

· Skill Gap

Several NBFCs may not possess adequate knowledge of data analytics. The demand for people who can work as data scientists and analysts is currently high, and the supply is less, making the appointment of people challenging. Continuous development of the employees and their exposure to advanced forms of analytics is critical in establishing an empowered and proficient workforce.

· Regulatory Compliance

Navigating the complex regulatory environment can be difficult. As a result, NBFCs must be extra careful to ensure that any analytics they undertake do not violate regulatory frameworks, including data protection laws. Therefore, a compliance framework with a provision for implementing analytics is needed to prevent any risk related to non-compliance.

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· Integration with Legacy Systems

Many NBFCs use incompatible systems that may not interconnect with up-to-date analytics. These technologies are expensive and time-consuming, and upgrading them can be very costly. This means that NBFCs should consider phased integration strategies, opting for less disruptive systems as they seek to adopt better systems.

· Cultural Resistance

Data analytics in decision-making can present challenges that stem from the usual organisational structures, where change is difficult, mainly when workers are used to a myopic way of thinking. Leadership should encourage the use of data and help employees deal with changes that stem from this approach.

Future Trends in Data Analytics for NBFCs

Data analytics in the NBFC sector is anticipated to be shaped by several major developments as the financial landscape continues to change:

· Increased Focus on Cybersecurity

With the increase in cyber-attacks in the financial sector, registered NBFCs must focus on security measures and resources to protect clients’ sensitive data. Protecting data and preserving customers’ trust will require acquiring modern cybersecurity tools and measures.

· AI for Improving Customer Experience

Natural language processing, specifically as applied to voice interfaces in customer service support like chatbots and virtual assistants, will continue to grow. These technologies enable customer support to solve immediate problems and make relevant suggestions to enhance customers’ experiences. Current reports reveal that NBFCs that embrace AI will be able to stand out in the market.

· Real-time Analytics

The demand for faster and more accurate real-time data analysis is also expected to rise, helping the NBFCs. In real-time, there is evidence of improvement in risk management, customer solutions, and an organization’s ability to adapt to market fluctuations.

· Collaboration with Fintech

NBFCs are expected to form mutual partnerships with new-age fintech firms to leverage their understanding of technology and next-gen solutions. Cooperation with fintech is also important since this can help improve the range of services provided, optimize work processes, and, ultimately, increase revenues.

· Sustainability Analytics

With growing environmental concerns, NBFCs may even seek sustainability indices for their analytics. The conventional method of rating NBFCs and evaluating the environmental impact of lending can help NBFCs improve their reputation among socially responsible investors.

Real-World Examples of NBFCs

Below are some real-world examples of non-banking financial companies:

• A case study of Bajaj Finance, an NBFC in India, has revealed that the company has greatly benefited from data analytics by improving its customer experience and risk management programs. The customer data obtained has identified new services targeted at a particular customer segment and enhanced the credit granting decision.
• Shriram Transport Finance has used an analytic model to predict likely defaults and manage collections. Understanding past trends and customer behaviour patterns and adopting suitable strategies has helped the company reduce NPAs and enhance portfolio quality.
• Mahindra Finance has adopted digital transformation and data analytics to extend its services to underserved populations. The company has utilized technology to create mobile applications and online platforms through which people can easily access financial services. At the same time, the company gathers the customer’s data for analytical purposes.

To Wrap Up

Thus, NBFCs in India have a perfect chance of changing by using data analytics as a tool. If these systems are correctly utilized, they would immensely help financial institutions improve their overall decision-making systems, customer relations, and understanding of the current trends in the financial market. Although there are some obstacles, the opportunities are vast; thus, data analytics is an indispensable tool for developing and preserving any NBFC.

As this NBFC sector grows from strength to strength, those organizations that approach data analytics most accurately and positively will stand to benefit in terms of efficiency and effectiveness more than their counterparts in the market. This ability to drive value results from NBFCs’ sound business models and the ability to leverage data remains a future focus area that NBFCs need to act upon today.

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