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Mining the Golden Opportunity in Retail Loans

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Mining the Golden Opportunity in Retail Loans Mining the golden opportunity in retail loans December 17, 2019 An ICICI Bank report powered by CRISIL An ICICI Bank report powered by CRISIL 1 In pursuit of retail growth prospect Objective Find out the growth prospect of retail loans market in India taking into account consumer financial behavior, technological progress and global trends Coverage • Likely evolution of asset classes in the retail loans market over the next 5 years • Key drivers and regulatory enablers • Evolving trends 2 In pursuit of retail growth prospect Sources Proprietary sources Primary sources Public sources •ICICI Bank’s current •Primary research with •Company disclosures portfolio and deep over 200 industry •Annual reports, CRISIL understanding of participants rating reports consumer finance •Consumer research •Credit bureau: Experian, category with 3,100 individuals CRIF-Highmark •CRISIL’s proprietary across 10 cities •Published data: RBI, SLBC, models NHB, SIAM •Global reports on other economies by renowned agencies Note: These standard sources have been used across the report 3 India treading the China growth path $ 20,000 65% 55 16,000 45 12,000 35 8,000 25 4,000 15 0 5 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 4 Retail loans The years ahead 5 Retail loans growth in India has topped other major economies in the recent past 20% 17% 16% 15% CAGR FY13 - FY18 10% 5% 3% 0% -1% -1% -5% India* China United States Germany United Kingdom Note: Retail loans includes – Normal housing loans, low-ticket housing loans, loan against property (LAP), MSME loans of less than Rs 2 crore ticket size, auto 6 loans, personal loans, credit cards, consumer durable loans, gold loans and education loans Retails loans estimated to double-up to Rs. 96 trillion in 5 years Rs. Trillion 120 100 Retail Advances 80 60 96 40 20 48 22 0 2013-14EFY14 2018-19EFY19 FY242023-24F Note: Retail loans includes – Normal housing loans, low-ticket housing loans, loan against property (LAP), MSME loans of less than Rs 2 crore ticket size, 7 auto loans, personal loans, credit cards, consumer durable loans, gold loans and education loans Increased demand projected for both consumption and investment-driven loans • Consumers more open to taking loans Rs. Trillion • Continuing trend of urbanisation and 110 nuclearisation 90 28 • Increased availability of data from traditional 70 and non-traditional sources 50 • Financiers leveraging technology and data 13 68 analytics 30 6 35 • Regulatory and legislative initiatives 10 16 propelling growth in low-cost housing loans -10 FY14 FY19 FY24P and MSME loans Investment Consumption Note: Investment includes housing, LAP, commercial vehicles, construction equipment, three wheelers, tractors and MSME loans of less than Rs 2 crore ticket size. 8 Consumption includes passenger vehicle, two wheelers, gold, personal, credit cards, consumer durables, education loans Healthy double-digit value growth foreseen across categories (1/3) Amount outstanding (Rs. Trillion) CAGR FY14E FY19E FY24P (FY19 - FY24) Normal housing loans 4.8 13 25.4 14% # Low-ticket housing loans 3.5 5.7 11.5 16% Loan against Property 1.6 4.7 9.2 15% Personal loans 1.1 3.9 10.5 22% Credit cards* 0.3 1.2 3.3 23% Note: # Average ticket size of low-cost housing loans: Metro less than Rs. 2.5 million, Non-metro less than Rs. 1.5 million; *No of fresh issued cards; 9 Healthy double-digit value growth foreseen across categories (2/3) Amount outstanding (Rs. Trillion) CAGR FY14E FY19E FY24P (FY19 - FY24) Commercial Vehicle finance* 2.6 4.8 9.1 14% # Passenger Vehicle finance 1.8 3.7 7.3 14% Two wheeler loans 0.2 0.5 1.1 16% Note: * Commercial vehicle finance includes new commercial vehicle, old commercial vehicle, construction equipment, tractors and three wheelers # Passenger vehicle finance includes new passenger vehicle and old passenger vehicle 10 Healthy double-digit value growth foreseen across categories (3/3) Amount outstanding (Rs. Trillion) CAGR FY14E FY19E FY24P (FY19 - FY24) MSME 3.1 6.6 13.2 15% Consumer durable loans 0.07 0.24 0.62 21% Gold 2.1 2.8 3.9 7% Education Loans 0.6 0.9 1.4 10% 11 Volumes to explode across retail asset classes (1/2) Average annual accounts Contribution to value added (mn) growth in next 5 years (%) FY17-19 FY20-24 Volume Ticket Size Normal housing loans 1.1 1.6 96% 4% Low-ticket housing loans# 1.7 2.9 103% -3% Personal loans 7.1 10 77% 23% Credit Cards* 7.5 9 77% 23% Consumer durable loans 21.7 52.7 70% 30% Note: # Average ticket size of low-cost housing loans: Metro less than Rs. 2.5 million, Non-metro less than Rs. 1.5 million; *No of fresh issued cards; 12 Volumes to explode across retail asset classes (2/2) Average annual accounts Contribution to value added (mn) growth in next 5 years (%) FY17-19 FY20-24 Volume Ticket Size MSME 0.6 1.8 77% 23% Passenger vehicle finance# 3.6 4.7 77% 23% Commercial vehicle 1.1 50% finance* 0.8 50% Two wheeler loans 16.0 23.9 60% 40% Total 60.1 107.7 Note: * Commercial vehicle finance includes only new commercial vehicle | # Passenger vehicle finance includes new passenger vehicle and old passenger vehicle 13 Systemic profitability to remain steady over the next 5 years Retail Loans RoA RoA in retail loans will remain steady 2.0% considering that: 1.7% 1.6% • Unsecured lending would grow 1.6% • Expansion into newer markets 1.2% where spreads would be better due to lower competition 0.8% • Adoption of technology would 0.4% lower TAT & Opex 0.0% FY19E FY24P 14 Five pillars driving the expansion Newer data sources Increasing Regulatory and Increase in digital Greater usage of to progressively competition could government lending to lower tech and data reduce risk in result in lower costs initiatives to drive costs for analytics to lower lending for consumers growth financiers opex 15 Newer data sources to progressively reduce risk in lending and aid volume growth; benefiting the companies • Access to a plethora of data points for credit assessment and process innovations brought about by technology • Lenders would be able to take instant decisions using data-driven automated lending models that have built-in checks and validations Income, assets and liabilities data Basic demographic data Data from GST filings Legal data PF contribution data Travel-related data Online purchases Mobile data Utility bill payments Health records PoS data 16 Increasing competition, resulting in lower costs; consumers will benefit • The consumers benefit with innovative products, better pricing in certain asset classes and the new found attention to smaller geographies Yield spread over G-sec rates 8.00% 6.53% 6.50% 6.67% 6.41% 5.90% 5.85% 6.00% 6.70% 5.80% 4.80% 4.90% Passenger vehicle 4.20% 4.00% 4.20% loans - new 3.70% 4.00% 4.50% 4.60% LAP 4.00% 3.40% Normal housing loans 2.90% 2.70% 2.00% 2.40% 2.00% 2.00% 1.50% Personal loans 0.00% FY14E FY15E FY16E FY17E FY18E FY19E Note: Spread for passenger vehicle loans – new, LAP and normal housing loans are over 10 year g-sec rates; Spread for personal loans are over 3 year G-Sec rates 17 Regulatory and government initiatives to help drive growth (1/2) • Remarkable success upon the implemented government initiatives in • SME Lending • Housing Loans • Reduction in Corporate Tax • Positive sentiment in the “to be launched/recently launched” regulations like • Reduction in corporate tax rates • The Co-Origination Model • Public Credit Registry 18 Regulatory and government initiatives to help drive growth (2/2) Digitisation of ownership of land title and legal Reduced TAT and verification documents, providing its access to financial costs institutions Industry-wide standards in data security, privacy Orderly market development and (PDP Bill), customer protection, shared data access, better customer profiling pricing Additional data points to lenders Continue thrust on digital payments for credit assessment Trust and authenticity of customer Enhance Cyber security capabilities data Scoping out the coverage for Encourage co-lending HFCs and non-PSL lending Create & test innovative new Innovation hub & environment products/ services 19 Digital lending to account for 16% of retail loans by FY24 - up from 6% currently Share of digital lending to remain higher for banks • Digital lending – defined as cases where loans 20.0% 19.0% are sourced, underwritten, and sanctioned 15.8% digitally – estimated at Rs. 2.7 trillion as of 16.0% March 2019 • Digital loans forecast to increase to 12.0% 10.3% Rs. 15 trillion (5-year CAGR of 41%), representing ~16% of retail lending in FY24 8.0% 6.4% 5.7% • Banks dominate the market, accounting for 4.4% 77% of total digital loans 4.0% 0.0% Overall Banks NBFCs FY19E FY24P 20 Share of digital lending highest in unsecured segment Share of digital lending (FY19) • Share of digital lending in overall account Unsecured loans 28% for ~6% in overall retail loans with the highest share in unsecured loans segment Others 7% • Within unsecured loans segment consumer durables has highest share of Auto loans 5% digital lending followed by credit cards and personal loans MSME 4% • Mortgage loans have minuscule share of digital loans Mortgage loans 1% 0% 10% 20% 30% Note: Unsecured loans includes personal loans, credit card and consumer durable loans; Auto loans includes passenger vehicle, commercial vehicle, two wheelers, three wheelers and tractors; Mortgage includes Normal housing, low-cost hosing and LAP; MSME includes both secured and unsecured loans to the 21 segment; Others includes gold loans and education loans Greater usage of technology and data analytics to lower opex,
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