Creating an end-to-end digital process for housing loans

Reported by: |Updated: February 27, 2020

HFFC believes that everyone should own a dream home. It blends technology and personalization to make loan processing easy and hassle-free. The company has a network of 65 branches across 11 states and Gujarat has 19 branches, Maharashtra 13, Tamil Nadu 9, Rajasthan 6 and Madhya Pradesh 5. The corresponding gross loan assets in these 5 states are 40.5%, 24.6%, 9.2%, 8.8% and 4.5% respectively. The other states where the company has a presence are Karnataka, Andhra Pradesh, Telangana, Chhattisgarh National Capital Region, Haryana and Uttar Pradesh.

SALARIED, SELF-EMPLOYED
The number of loan accounts of the company has increased to 37,086 as of Q2, 2019-2020 from 9747 as of 2016-17. During the same period, the share of salaried loan accounts has increased by 74.4% from 68.5%. Out of the gross loan assets (GLA) of Rs31.13 billion, as much as 72.6% is related to salaried customers and 24.6% to self-employed. The company’s CEO Manoj Viswanathan customers from the low and middle-income groups accounted for 69% of the GLA and the value of home loans has increased to Rs28.47 billion in September 2019, recording a yoy growth of 61.3%.

VARIETY OF LOANS
Home First Finance primarily offers loans for the purchase or construction of homes, against property, developer finance and loans for the purchase of commercial property. These segments comprise 91.5%, 4.7%, 2.8% and 1.1% respectively of the company’s GLA as of Q2 2019-2020. While the GLA in the form of housing loans has increased to Rs28.47 billion (CAGR 66%), loans against the property has increased to Rs1.46 billion (CAGR 92%), developer finance loans to Rs866 million and loans for purchase of commercial property to Rs328 million. Says Viswanathan: “We have passed on the PMAY CLSS subsidy to more than 17,000 customers, roughly amounting to Rs4.2 billion.”

LOAN MELAS
The company has tie-ups with most of the renowned affordable home developers across the cities where it is present. It organizes loan melas to reach out to the end-users directly. This also gives the company a good platform to engage with the customers and understand the pulse of the market. Viswanathan explains: “We try and bring in the entire ecosystem in these melas, where a customer can get all their questions regarding their home loan, property valuation, legal issues, etc answered. We haven’t experienced a slowdown in demand in our segment, but it has not grown as fast as we would have liked.”

MANAGING ALM
Affordability of homes has improved in the last few years because of real estate prices remaining stagnant and rising incomes. As a result, housing finance market experienced healthy growth in housing loan outstanding of approximately 20% during 2015-2019. This has been mainly on account of rise in disposable income, healthy demand and a greater number of players entering the segment. With tightened liquidity post-September 2018, housing finance companies have encountered structural challenges in the form of increased refinancing risk and asset-liability mismatch, which slowed down disbursements in fiscal 2019. Viswanathan says access to funds from the debt capital markets has also declined a bit, especially for those companies with high negative ALM mismatches; thus several players in the industry have been focusing on managing ALM rather than growing their book. First-time borrowers, who currently account for around 60% of the market by value and 70% by volume, would continue to drive the growth, he emphasizes.

CLOUD, CRM, LMS
Home First Finance has established a differentiated technology framework with customized systems and tools thereby enhancing convenience for customers, increasing operational efficiency as well as reducing turnaround times and transaction costs. It has integrated its systems with third-party databases to obtain additional customer data. Says Viswanathan: “During the last 3 financial years, we invested Rs147 million in our information technology systems. We capture and store all our data on a cloud services platform, which helps in the usability and accessibility of such data, results in cost savings and improved underwriting practices. Our integrated customer relationship management and loan management system provides us with a holistic view of all our customers and ensures connectivity and uniformity across our branches. We utilize proprietary machine learning and customer scoring models to assist us with our credit assessment process. The integration of such data across platforms enables us to process loans in a paperless manner and with a low turn-around times.”

DATA LAKE, ANALYTICS
The company offers mobility solutions through dedicated mobile applications for its customers to enable quick and transparent loan-related transactions, as well as for connectors who generate leads for it. Viswanathan says as of 30 September 2019, the company’s customer mobile application had approximately 16,200 monthly active users comprising approximately 44% of the total customer base. The company uses a data lake, which helps it with all the stages of the data life cycle of loan consolidation and visualization. It also facilitates machine learning model development and implementation. “Today, we can perform real-time analytics to generate customized reports and make better operational decisions,” he adds.
The company also uses an application for geotagging of properties and a machine learning backed property price predictor. Viswanathan claims this has helped the company reduce its TAT for approving loans, as well as achieve higher accuracy in determining the loan-to-value ratio. The key technology partners to the company include Actify, Experian, Karza, Perfios, Ormax, Paisa Bazaar, and Homelane.

ROBUST COLLECTION SYSTEM
The company has set up a robust collection management system. Substantially all of its collections for the financial year 2019-20 are non-cash based and it employs a structured collection process. This is done through automated calls and text messages. Says Viswanathan: “This eases stress on monitoring financial transactions and reduces our cash management risk. All our borrowers register for an automated debit facility and we track the status of installments collected on a real-time basis through a collection module in our system. We perform predictive analytics to predict the probability of default, which helps us in obtaining early signals of potential defaults and initiate appropriate action to mitigate risks. Our collections process is completely managed by our branch teams and a significant portion of our front-end team incentives are also dependent on collections.”
The severity of HFFC’s action increases as the number of days where an amount is due increases. As of 30 September 2019, the company’s 30 days past due was at 1.6% and 90 days past due was at 0.9% of its GLA.

CAPITAL RAISING, IT PLANS
The company expects to raise Rs15 billion from the market. The IPO comprises a fresh issue of Rs4 billion and Rs11 billion offer for sale by promoters and investors.
Viswanathan says the company intends to continue to scale up its business and improve its profitability through key strategies. “We will leverage technology to grow business and drive operational efficiency. We will continue to strengthen and invest in technology to accelerate our growth, improve customer experience and continue to achieve industry-leading turnaround times in our operations. We are focused on creating an end-to-end digital process for housing loans via exhaustive customer data capture through API integration with third-party databases, automated underwriting via machine learning algorithms and instant approvals through mobility solutions,” he elaborates.
The company plans to expand its business in a contiguous manner. “This high-density model would allow us to grow our business with lower costs and increase our profitability. We have set up a scalable operating model for expanding operations with lower incremental costs to drive efficiency,” says Viswanathan.
He continues: “Our risk management initiatives will include obtaining a better understanding of the geographies in which we intend to expand to, improving the credit scoring models and algorithms currently deployed, improving our collection techniques and our property underwriting procedures.”

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