Good questions @devenmehta2006. CRISIL too has its own fears. It expresses confidence that the parent Bajaj Holdings with substantial liquid assets will be able to step in to save Bajaj Finance in case of a crisis. In CRISIL we trust.
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Zero cost EMI earn higher rate of 16-18%. Some of the mortgage and Rural loans might also be above 15%. If we take all financial assets on balance sheet and consider average assets, the interest/assets come to 15.7% (Approx figure I calculated from March 19 consolidated numbers). So the numbers will match up if we get actual break up.
Q2 FY20 Results
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Summary from investor presentation:
- We continued to remain focused on growth, profitability & sustainability. We continued to strengthen our business model in Q2 FY20 as well.
- AUM grew by 38% YoY to ₹ 135,533 crore as of 30 September 2019. New loans booked grew by 23% YoY to 6.47 MM. Overall, Q2 FY20 has been slower than Q1 FY20 on demand outlook.
- AUM growth was granular for most lines of businesses in the company (YoY growth for Consumer B2B sales finance 19%, Consumer B2C 46%, Rural B2B 35%, Rural B2C 62%, SME 34%, Mortgages 43%, Auto Finance 61%, Commercial excluding Securities lending 18%, Securities lending 11%).
- New customer acquisition momentum for the quarter remained strong at 1.92 MM. Total customer franchise stood at 38.70 MM as of 30 September 2019, a growth of 29% YoY. Cross sell franchise stood at 22.78 MM as of 30 September 2019. Strategy remains to grow wallet share of these 22.78 MM clients.
- Existing customers contributed to 70% of new loans booked during Q2 FY20 versus 66% in Q2 FY19. Existing customer share was higher due to tightening of credit standards across most sales finance businesses in Q2 FY20.
- We added 102 locations in Q2 FY20 taking our geographic presence to 1,997 locations in India as of 30 September 2019.
- Overall cost of funds at consolidated level has sequentially improved by 11 bps to 8.38% in Q2 FY20 from 8.49% in Q1 FY20. Reduction in cost of funds is majorly due to good ALM management, strong liquidity position and incremental borrowings being sourced at much lower cost.
- Consolidated liquidity buffer (free cash and liquid investments) stood at ₹ 8,043 crore as of 30 September 2019. We remain very comfortably placed on liquidity.
- During the quarter, the Company entered into facility agreement with various banks to avail ECB loans upto USD 575 MM in one or more tranches. First tranche of USD 276 MM (equivalent to ₹ 1,959 crore) was drawn on 17 October 2019.
- Deposits book stood at ₹ 17,633 crore, a growth of 60% YoY. Its contribution to consolidated balance sheet stood at 14.8% as of 30 September 2019. We continue to increase investments in new channels to grow retail deposits. We now have over 7 different channels to grow deposits.
- We continued to maintain strong focus on growing our fees and commission revenue pools. Our fees and commission income in Q2 FY20 grew by 66% YoY.
- Loan loss and provisions (expected credit loss) grew by 89% YoY in Q2 FY20. Loan loss to average asset under finance (AUF) stood at 1.86% in Q2 FY20 in line with Q1 FY20.
- Consolidated operating expenses to net interest Income improved to 34.6% in Q2 FY20 as against 35.4% in Q2 FY19.
- Overall NIM remained strong in Q2 FY20 as well.
- Gross NPA & Net NPA stood at 1.61% and 0.65% as of 30 September 2019. Sequentially, the movement in GNPA and NNPA is 1 bps and 1 bps respectively. Adjusted for IL&FS exposure, GNPA and NNPA stood at 1.44% and 0.52% as of 30 September 2019.
- Consolidated profit before tax for the quarter grew by 41% YoY to ₹ 2,022 crore. Consolidated profit after tax for the quarter grew by 63% YoY to ₹1,506 crore owing to 41% YoY growth in PBT and benefit of reduced corporate tax rate from 34.94% to 25.17%. The consolidated profit after tax for
the quarter includes a net gain of ₹ 1 crore for previous periods which consists of a benefit of ₹ 183 crore due to lower corporate tax rate pertaining
to Q1 FY20 and a charge of ₹ 182 crore on account of re-measurement of deferred tax asset. - Annualized Return on Assets for the quarter was 4.71% and Return on Equity was 28.00%.
- Capital adequacy ratio stood at 19.68% as of 30 September 2019. Tier 1 capital stood at 15.86%. Consolidated leverage stood at 6.6X.
- On 17 September 2019, the Board of Directors has approved, subject to the approval of shareholders, issue of securities for an aggregate amount up to ₹ 8,500 crore through Qualified Institutions Placement to Qualified Institutional Buyers in accordance with SEBI (Issue of Capital and Disclosure Requirements) Regulations, 2018 as amended.
- Standard assets provisioning was at 91 bps (ECL stage 1 & 2) under Ind AS as against requirement of ~40 bps as per RBI and NHB.
- Bajaj Housing Finance Ltd continued to grow in a robust manner delivering a profit after tax of ₹ 130 crore in Q2 FY20
- Bajaj Financial Securities Ltd has commenced its broking and depositary service business during Q2 FY20.
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Assume 1.2 Billion population. Out of that, 40% maybe from age 21-50, potential customers. So, about 500 million. Take 50% out - as two people from the same household may not get different loans. About 250 million potential customers. Of that, assuming bottom 40% and top 10% won’t be reaching out for loans, you are taking about 125 million potential customers. They currently have ~35 million customers. +/- 20% margin of error. Back of the envelope calculation.
Gives some idea. https://www.medianama.com/2019/03/223-india-had-44-2m-credit-cards-958-2m-debit-cards-in-december-2018/
Walk into a showroom, tell them you don’t have income proof, and see what they say!
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35 million out of 125 million potential customers already. In other words, will they reach saturation point with just two doublings or say in 6 years?
They have huge potential for cross selling different products to these 35 million customers and still deliver a decent growth
According to one Report the lending business in India will be 600 billion dollars in 2026, and the current AUM of Bajaj Finance is 15 billion dollars. The scope is huge going forward as Bajaj Finance has emerged as the strongest player so it is going to be the sector leader in 2026. Look at the long term story friends. The big picture is so big that in the hindsight people with myopic view will regret on losing this opportunity.
Which report ? Can you please share that if you have it.
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Mr. Nandan Nilekani had mentioned this 600 billion dollar figure by 2026 in his presentation 2 years back. That presentation video is on YouTube. I am sorry for not able to provide you that video link because I do not remember exactly which presentation was that. But I will try to find out that video and share the link over here. I apologise for not being able to share the link right now.
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Got a promotional email from BAF that now groceries can be bought on no cost EMI from partner outlets. One of the parteners listed was Big Bazaar. Not sure about how this is going to play out.
My guess is BAF’s lending for groceries, medical treatment etc is like pending personal loans based on credit history for a fee from the partners.
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@hitesh2710 Hitesh bhai, @basumallick dada, @dd1474 An novice query that i have is since a lot of loans that NBFC gives is unsecured, how does NBFC like Bajaj Finance manage to recover these loans so efficiently and keep NPA low in a fantastic manner ? Request you to pls help understand
Thanks
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Credit decision involve assessment of borrower on two parameter, 1) Ability to pay and 2) Willingness to pay.
Ability of borrower would change over a period of time based on his income/cashflow, the willingness is character which remain unchanged over the life.
So it is not that secured loan are better than unsecured loan, if willing and ability is good, even unsecured loan recovery could be better.
So, it is also selection of customer which is critical. Bajaj Finance in my limited understanding use various information avaible from Credir bureau report to Mobile/Electricity bill to indentify the right segment whom it want to lend. Focus on correct segment along with good underwriting standard have assisted the company to manage NPA. You may go through their presenation to understand in detail their credit assessment and underwirting process.
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Just to add to what Dhiraj has said, in India the general retail population has a good credit history and culture. People usually take loans and return them. Companies like Bajaj Finance and other such banks use large data sets about individuals including their credit history, social media profile, geographical and demographical data and also the data related to the product/service being bought. For example, historical data suggests that defaults on home appliance loans are very low or a combination of your age, product you are buying are scored. There is a very large filed of analytics study now termed as credit risk analytics, where the main job is to create patterns.
The other important aspect to remember is that these companies are spread-betting. They are giving thousands or millions of such small loans and the cost of statistical default is already baked into their rate structure. i.e. they predict that for a particular sub-category of loan, x% will become NPA and y% will need follow-ups and/or restructuring etc.
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Thanks so much @basumallick dada and @dd1474. This was very helpful .
Is there underwriting by nbfc too like insurance companies? How does it work ?
Pls pardon my basic query but Could you pls elaborate on this @basumallick dada ? How do they ensure that statistical default is not very different from actual as statistical is historic?
Very insightful
Lending these days is a very sophisticated activity. That is one reason why all large financial companies have centralised loan processing. The use of big data, statistical modelling and fraud management & detection algorithms is used extensively in most progressive companies globally.
The systems use data across the systems to “understand” and then signal if there are any deviations from the baseline assumptions. For example, if lets assume I model for a 1% NPA in 2-wheeler loans and I find a particular instance where it is going above the threshold, then I will try to determine the reason by analysing the data. If there are patterns in the data that is recognizable, I will take appropriate action. Say, I find that people in one district are not paying on time and then I realize that that area had recently had a flood problem, then I may decide to i) go slow on further credit sanctions to that particular area, ii) step up vigilance and follow ups in that district and iii) recognise the problem and decide to give the creditors some more time or restructure their credit for the short term, i.e. take part payments etc.
Big data usage is increasing in the financial world in ways common people are not even aware. Simple examples in insurance like lesser premiums for non-smokers or people who walk a certain number of steps etc are already there. Similarly, banking also is using data from social media, credit scores, consumption patterns, weather patterns, demographic patterns etc.
Think about it. Your banker and your telecom operator at any given time know these about you: where exactly you are based on your tower location, how much you earn, how much you spend, how you spend, how many times you withdraw cash from ATMs, which ATMs, which site you use to shop online, what amount you spend in buying online or through large format retail, how frequently you eat out, how many movies you watch, if you have any loans, how many loans, who has given you the loans, who you transfer money to regularly, who transfers money to you…
It’s scary… I doubt even if google / facebook has as much important data about your life as your bank does! But I completely digressed from the point 
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Got it perfectly , Dada . This was very insightful
Not at all , in fact much useful and appreciated . In fact due to that only, i could understand the relevant point . And anyways Charlie Munger too advocates multidisciplinary thinking moving across subjects
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@A_shah
In case of lending to retail/SME/corporate, the lender assess various risks, mainly Credit risk and market risk. Market risk are boardly macro factors like change in exchange rate/ interest rate which affect business performance. Credit risk involve assessing credit payment ability of borrower, which include business risk (industry in which operate like cyclical etc) and financial risk.
While for Corporate lendening (most of large borrower), one does detail assessment of credit risk and those people are generally called buy various name from Credit manager to Risk manager.
In case loan are to SME/Retail, it would not worth that detail effforts on each account. Hence, instead of evaluating individual account, broad parameter like Salary/Self employed, Area of operation, (City), Type of residence: Permanent/temperory, Past credit history and various other parameters are deternime to develop a model. Generally, these model require some inputs on submission of which one get score which decide whether the customer is eligible for borrowing, and if yes, at what rate? The person who is resonpsible for develope and monitoring these model are called credit underwriter in my limited understanding.
Hope this answer your query. Other members, in case I have misrepresented the fact, please correct.
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