SME Finance: A Generational Shift

Transcription

1 SME Finance: A Generational Shift FIBAC Conference 20 AUGUST 2018

2 Special Thanks 1

3 No one will ever give someone like me a loan Turnover: Customer: 15 lacs pa, 25% margin 400 GV Khenat Newspaper & Magazine Distributor Avg. bill: Bank: Pain point: Rs per month Dena Bank Cash collection 2

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8 GV Khenat 7

9 Khenat wishes to expands his business and hence needs loan of Rs 50K. He downloads the SME Banking App 8

10 PROVIDE PERSONAL DETAILS Ganesh Vijay Khenat Poddar House, Dr. AB Road, Worli, Mumbai Aadhar No: PAN No.: DATPS4675K GET LOAN NOW 9

11 PROVIDE PERSONAL DETAILS Ganesh Vijay Khenat Poddar House, Dr. AB Road, Worli, Mumbai Aadhar No: PAN No.: DATPS4675K GET LOAN NOW 10

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24 API framework must exist for all institutions who own Customer Data Bank BANK Tax authorities Electric & power companies Insurance companies Telecom Industries Central consent Authority Ecommerce companies Lender 23

25 GST has pushed formalization % formal 1 MSMEs (INR >3 Lakh in turnover) 61 +5% % % <1% 99 Overall Nano: <10L Mini: 10L-1Cr MSMEs: 1Cr+ GST registration 18% 65% 96% Pre-GST formal 1 Post-GST formal 1 1. Existence of a formal record through registration with any one Government act/authority like GST, EPFO, Factory Act, Municipal/Local corporation etc. Source: Quantitative survey analysis (N=1514): Data weighted to be representative of (3L+ turnover) MSME universe w.r.t Turnover x Sector X Geo (U/R), BCG analysis 24

26 Finance is not the answer to all problems Securing demand is a major challenge MSME pain points (% MSMEs, top 4) Current wave of formalization is forced Reasons to formalize (% MSMEs, top 4) 33% 29% 50% 19% 19% 4% 7% 7% Getting loans Demand issues 1 Cost of credit Predicting cash flow Mandated by law To get govt. support Fear of audits Benefit in registering Source: BCG Omidiyar Network quantitative survey analysis (N=1514): Data weighted to be representative of (3L+ turnover) MSME universe w.r.t Turnover x Sector X Geo (U/R), BCG analysis 25

27 Pull-based formalization can lead to a virtuous cycle; potential for breakout growth Strong government push leading to formalization 1 5 Formalization with digitization creates digital footprints Digital footprint enables access to cheaper credit 2 3 Potential to trigger takeoff in formal credit demand 4 More MSMEs to rapidly formalize/ digitize as they realize benefits 1. P&L scenario modeling analysis conducted to compare margins under formal and informal economy to understand tax vs interest cost savings tradeoff (Assuming informal credit cost = 3% p.m., formal = 1.2% p.m., 18%) Source: Quantitative survey analysis (N=1514): Data weighted to be representative of (3L+ turnover) MSME universe w.r.t Turnover x Sector X Geo (U/R), BCG analysis 26

28 MSME lending in individual name growing fastest 39 L Cr (39%) 101 L Cr 15 L Cr (15%) 10 L Cr (9%) 37 L Cr (37%) Total Large Corporate Borrowing 1 MSME Borrowing in Entity name 2 MSME borrowing in Individual Name Retail and Agri Borrowing 3 CAGR 4 5% 13% 25% 22% Notes: 1. Large corporate borrowing includes entities with cumulative outstanding borrowing of greater than 50 Cr. 2. MSMEs borrowing in entity name include loans take in the business name (in commercial bureau) with cumulative outstanding borrowing of <50Cr 3. MSME borrowing in individual name include loans individuals in retail credit bureau analytically tagged as borrowing for business purposes 3. Retail and agri borrowing includes remaining individual borrowing in the retail credit bureau 4. Mar 16-Mar 18 CAGR Source: TransUnion CIBIL analysis, BCG analysis 27

29 Sweet spot in 2 L 1 Cr ticket size? Formal Credit Growth 1 (%) 20% 30 24% 20 17% 11% 10 22% 7% 18% 10% % gross delinquency % % 5 0 Loan Slabs 2 (INR) <2L (Nano) 2-10L (Mini) 10L-1Cr (Micro) 1-10Cr (Small) 10-50Cr (Medium) 0 1.Mar '16-Mar '18 CAGR 2. Consist of borrowing both in entity and individual name. Loan slabs for entity level borrowing defined at an enterprise level, basis the maximum credit exposure in past 8 quarters, loan slabs for individual level borrowing basis individual loan size 3. Delinquencies calculated basis accounts in 90+ DPD (As of Mar'18). 28 Source: TransUnion CIBIL data and analysis; BCG analysis

30 PSB share of MSME finance is now below 50% % of Credit Exposure by Lenders (<INR 10L) (INR 10-50L) (INR 50L-1Cr) Overall MSME 1 53% 49% 35% 30% 52% 44% 50% 41% 21% 22% 34% 37% 28% 30% 26% 30% 1% 6% 19% 21% 7% 1% 5% 25% 26% 1% 6% 2% 10% 8% 12% 10% 3% 3% 14% 7% 15% 9% 4% Mar 16 Mar 18 Mar 16 Mar 18 Mar 16 Mar 18 Mar 16 Mar 18 PSU Private Banks NBFC Foreign Banks Others Notes: 1. Overall MSME institution wise share includes all MSME exposure till INR 50 Cr including entity level exposure and individual level exposure Source: TransUnion CIBIL data and analysis; BCG analysis, Data represents MSME borrowings in both entity name and individual name 29

31 Interventions from government and RBI Continue to strengthen India stack (Accelerate consent layer, Raise ekyc OTP limits, reinforce esign legality, improve enach quality, build UPI 2.0 collections features) Democratize data access with consent (Mandate all institutions with customer data to share through standard API framework) Strengthen registration mechanism (Expand MCA to include proprietorship and partnerships, standardize SIC for streamlined reporting and benchmarking) Expand credit bureau infrastructure (Augment bureaus with surrogate data, mandate bureau quality and submission) Build incentives for digital transactions (Incentivize digital payments, strengthen dispute resolution and build deduction at source/escrow in UPI) Reforms for ease of business (Streamline labour laws and permits, simplify taxation to reduce admin burden with size) 30

32 DISCUSSION POINTS 31

33 Bank account coverage has dramatically transformed Current 77Mn 21% Accounts 1 60 Mn MSMEs Growth rates 2 2.5X post demon 40% Active on Internet Banking 5 Savings Accounts 1 1.6Bn 80% 330 Mn Fin Inclusion 4 PMJDY, 63% (Lower Mid active 3 Income) 18% Active on Internet Banking 5 1. CA and SA numbers Mar 2018 estimate, based on 2017 data projected using growth rates from BCG FIBAC survey 2. Post demonetization growth in current accounts in post demonetization vs Active is atlelast one active transaction in last 6 months (includes both PMJDY and BSBD) 4. % of people (aged 15+) with FI account (avg), lower middle income GNI/capita USD 995-USD 3, BCG FIBAC survey, data for 2018 Source: PMJDY website, RBI database, Global Findex Survey, World Bank, BCG Analysis 32

34 Productivity enhancement with connectivity "shock" Reducing data cost 280 1/20th 13 Avg. cost/gb data (INR) Decreasing smartphone cost Avg. smartphone prices (USD) 260 2/3rd 170 MSME smart phone penetration 45% 85% Percentage of MSMEs with smartphone +40% Mobile data consumption Sources: Credit Suisse report India Market Strategy, 2018, The Mobile Economy: GSMA, IDC Quarterly Mobile Phone Tracker - Final Historical, 2018Q2, BCG 2015 B2B survey; BCG analysis

35 Digital payments: 3 years leapfrog due to demonetization much bigger jump expected in next 3 years Total digital transactions 1 (Mn/month) 1, Nov '16 (Demonetization) ~3 year leapfrog 920 ~450 Mar '18 1.Credit Card, Debit Card, PPI and UPI transactions included for calculation. Red line represents date of demonetization announcement Source: RBI database, NPCI, betterthancash.org, BCG analysis Gigantic leap expected with social media E.g. China mobile payments grew 16X in 3 years post Tenpay WeChat integration 34

36 Huge number of new data sources Financial and tax data GST ITR TDS (Form 26AS) Service Tax TIN Credit bureau data Entity level credit data (Commercial bureau) Individual credit (Consumer Bureau) Utility data Electricity Telecom Gas and Internet Entity data MCA Udyog Aadhaar Company website EPFO Individual data PAN Voter Id Driving License Professional Reg. Mobile and social data SMS data Geo-location Call logs Phone hardware data Shop Act ESIC SMERA rating Social footprint Account verification Social profile & connections Notes: Representative list, EPFO: Employee s Provident Fund Organisation, ESIC: Employee s State Insurance, TIN: Taxpayer Identification Number, SMERA: SME Rating Agency of India 35

37 Huge amount of data on each borrower 70+ data sources available Sources Bank accounts Bureau GSTN MCA MB+ digital data per MSME Data size (in MB) 25 MB 1-2 MB MB MB data variables Number of variables Analytics to handle large quantity of data Preserving past data for model build and validations Robust data architecture and governance 36

38 Multiple digital enabler models, supporting digital lenders, have emerged API aggregators Data extraction and analytics platforms Surrogate data providers Alternate data platforms and scoring models Digital process enablers 37

39 Primary sources of credit insight set to change Audited Financials Relative weight of metrics Traditional Credit Assessment New Gen Digital Assessment Account operations analytics Bureau Borrower/Management Profile Notes: Bubble sizes represent relative weightages for the data source Source: BCG case experience, expert interviews 38

40 Digital alternatives will significantly reduce TATs Customer data Traditional (5-7 day TAT) Manual form filling Digital (~hours) Digital data capture/autofill KYC Account Transaction Analysis Fraud detection Agreements Paper based Manual In-person field visits/telephone based Wet Signature ekyc Automated Surrogate data based esign Repayment PDC based enach/emandate 39

41 Banking industry is investing heavily in technology Capital expenditure on IT assets (Rs. in crores) +28% +56% 3,248 3,783 4,153 1,488 1,776 2, % % PSU - Large PSU - Medium Private - New Private - Old FY16 FY17 FY18 Note: 1. Data of 6 PSU Large banks, 11 PSU - Medium banks, 4 Private - New banks and 7 Private - Old banks has been included for the purpose of this analysis Source: FIBAC Productivity Survey 2018; BCG analysis 40

42 Banks are building digital and analytics teams Digital 1 and Analytics 2 Staff per 1000 FTEs 3 +44% +15% % % % PSU - Large PSU - Medium Private - New Private - Old Industry Analytics 2 Digital 1 1. Digital staff includes digital marketing staff, social media staff, digital design staff, digital customer experience staff, digital banking channels staff and business IT team 2. Analytics staff also includes in business intelligence unit staff, IT staff and management Information system staff 3. FTE stands for full-time equivalents Notes: 1. The total FTE includes the staff of captive subsidiary as well as the outsourced staff 2. Data of 4 PSU - Large banks, 12 PSU - Medium banks, 4 Private - New banks and 5 Private - Old banks has been included for the purpose of this analysis Source: FIBAC Productivity Survey 2018; BCG analysis 41

43 Credit and risk staff as a % of total staff Copyright 2018 by The Boston Consulting Group, Inc. All rights reserved. Banks with larger credit and risk teams seem to have better control over NPA Centralized credit and risk staff as a % of total staff in FY18 vs Gross NPA in retail and MSME segments in FY18 (%) High 6% 4% Public Sector Banks Private banks 2% Low 0% 3% 6% 9% 12% 15% 18% GNPA in retail and MSME segments Low High Note: 1. Data of 5 PSU - Large banks, 10 PSU - Medium banks, 4 Private - New banks and 5 Private - Old banks included for the purpose of this analysis Source: FIBAC Productivity Survey 2018; BCG analysis 42

44 Indian customers prefer a hybrid approach to digital 24% 34% 15% 13% 9% Digital 76% 38% 51% 80% 82% 86% Hybrid 18% 6% 38% 16% 5% 5% 5% Face to face Netherlands Japan USA China India - Pvt. New India - PSB Note: 1. Based on responses received from survey of ~60,000 respondent globally; 2. Digital customers are those who uses digital channels for transactions at least once in 3 month and have visited a branch less than once or never for a transaction; Hybrid customers are those that uses digital channels for transactions at least once in 3 months and also visits branch at least once in 3 month for doing a transaction, and face to face customers are those who visits branch for a transactions at least once in 3 month and have used digital channel less than once in a year for transactions Source: Data for India as per REBEX consumer survey 2018; data for global based on REBEX consumer survey 2016 covering 16 countries 43

45 MSME segments vary in adoption of digital Enterprises (%) Non-Trade sector usage of internet 100 Hotel & Restaraunts Educational Activities Financial Activities Transportation, Travel Agency Self-Employed Professionals 50 0 <INR 2L INR 2L 5L INR 5L 10L INR 10L 25L INR 25L -1Cr >INR 1Cr Turnover slabs of MSME Source: MOSPI Government survey data

46 Imperatives for banks and NBFC Embrace data for credit and fraud risk Store, analyze, model Frictionless customer journey Digitization Segment specific solutions Different score cards, different source of data, different journey Partnerships For access to customer, for data Assisted digital with human touch Digital with solutions for channel partners, hand holding customers during process Customer Education Financial discipline, digital adoption, 45

47 Growth in MSME credit (FY18 over FY17) Significant regional disparity exists in MSMSE credit growth Quartile 1: >10% Quartile 2: 0% to 10% Quartile 3: -7% to 0% Quartile 4: < -7% Haryana Punjab Gujarat Rajasthan Jammu and Kashmir Daman and Diu Dadra and Nagar Haveli Maharashtra Madhya Pradesh Himachal Pradesh Chandigarh Uttarakhand Delhi Uttar Pradesh Chhattisgarh Orissa Assam Bihar Meghalaya Jharkand Telangana Sikkim Tripura West Bengal Arunachal Pradesh Nagaland Manipur Mizoram Goa Karnataka Lakshadweep Kerala Tamil Nadu Andhra Pradesh Puducherry Andaman and Nicobar Islands Note: Data of 15 public sector banks and 7 private sector banks included for the purpose of analysis Source: FIBAC Productivity Survey 2018; BCG analysis 46

48 Mobile banking activation (FY18) Southern states outshining the rest of India in mobile banking adoption in savings accounts Quartile 1: >5% Quartile 2: 3% to 5% Quartile 3: 2% to 3% Quartile 4: < 2% Daman and Diu Dadra and Nagar Haveli Lakshadweep Andhra Pradesh Arunachal Pradesh Delhi Sikkim Rajasthan Uttar Pradesh Assam Bihar Meghalaya Nagaland Gujarat Manipur Tripura Jharkand Madhya Pradesh West Bengal Mizoram Goa Karnataka Jammu and Kashmir Himachal Pradesh Chandigarh Punjab Uttarakhand Haryana Maharashtra Kerala Chhattisgarh Orissa Telangana Puducherry Tamil Nadu Andaman and Nicobar Islands 1. Active account defined as an account with at least one user initiated transaction in the last six months (as of 31st March 2018) Note: Data of 15 Public sector banks and 7 private banks included for the purpose of analysis. Source: FIBAC Productivity Survey 2018; BCG analysis 47

49 Rapidly changing transaction profile in banking Total transactions in FY16, FY17 and FY18 (%) Growth in total transactions (%) Number of transactions ('00 crores) % % 5% 22% 11% 11% 8% 7% 2% 8% 6% 14% 1% 7% 43% +20% 35% 5% 2% % 21% 10% 9% 5% 30% 3% 4% Mobile ECS 1 POS Internet NACH UPI 2 NEFT (Branch) Cheque Cash ATM 3 Digital channels Branch based 4 ATM 3 FY16 over FY15 67% -4% 15% FY17 over FY16 94% -19% 6% FY18 over FY17 48% -11% -5% FY16 FY17 FY18 1. ECS transactions can be initiated offline or through online channels 2. UPI did not exist in FY16 3. ATM/CDM includes withdrawals transactions at ATM and deposit transactions at CDMs. ATM and Mobile transactions included are financial transactions only 4. Branch based channels include cash and cheque. Cash transactions refer to counter cash transactions within branch Notes: 1. Data of 5 PSU - Large banks, 8 PSU - Medium banks, 3 Private - New banks and 3 Private - Old banks included for the purpose of cash transactions and NEFT transactions at branch 2. FIBAC data is used for NEFT from branches, Counter cash and E-POS transactions Source: RBI data, FIBAC Productivity Survey 2016, 2017 and 2018; BCG analysis 48