India s NPA Predicament

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Meryl Robbins
5 years ago
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1 India s NPA Predicament Managing Credit Risk to accelerate growth Executive Summary Rising number of frauds and bad loans have shaken the banking sector of late with the risk of capital erosion increasing day by day. Credit is an important part of a country s growth and the lack of it, due to rising NPAs, would not only affect a bank s profitability but also the economy, as problems in the banking sector easily cascade on to other sectors bringing down overall job opportunities. In this paper, we will shed some light on the current NPA crisis in India, by looking at the recent high value NPAs and frauds and analyse the industry trends While the Reserve Bank of India (RBI) is bringing in various reforms, financial institutions themselves need to tighten their grips on the problem by being proactive in tracking Special Mention Accounts, Non-Performing Assets and possible cases of frauds or face the risk of a glooming economic depression which could push India s growth a decade back. A key take-away from this paper would be our point of view on how an NPA management system should be envisaged w.r.t the RBI guidelines, the various challenges (both internal & external) that a financial institution might face, manage asset lifecycles and keep up to the changing industry and regulatory dynamics.

2 India s Current NPA Crisis Not long ago, Indian banks were growing at a rapid pace with overall credit growth ranging from 10-15%. However, the constant need of increasing overall credit & the drive to increase financial inclusion has resulted in a huge pile of Non-Performing Assets (NPA). Growth in gross advances for Scheduled Commercial Banks (SCB) as of March 2018 is at 6.9% which has slightly improved from the 6.2% figure of September The current Gross Non-Performing Asset (GNPA) figures are estimated at over 10 lakh crores putting India in 5th position worldwide in terms of NPAs. In the past 5 years, 23,000+ frauds have been reported amounting to Rs 1 trillion. To put things in perspective, the amount of bad loans in India is more than the GDP of over 100 countries. Chart 1 below gives a trend on Gross NPA in India for all banks and Chart 2 gives a trend on Gross NPA % for the industry. Chart 1: Trend Analysis Gross NPA (All Banks) in Rs. Cr Chart 2: Trend Analysis Gross NPA % (All Banks) RBI has further predicted that the GNPA ratio might go as high as 12.20% by March The above-mentioned volume of NPA has resulted in banks making higher level of provisioning thereby eroding huge amounts of profits. The problem doesn t stop here as the bank s ability to lend money has also taken a hit with RBI s lending restrictions under PCA. As lending decreases, a cascading impact would be seen on other sectors which could result in lack of job opportunities and slowing down of the economy.

Recent Banking Frauds in India NPA and banking frauds are not new to India but have recently come into everyone s attention due to certain high value banking frauds discovered in the past few months

3 Recent Banking Frauds in India NPA and banking frauds are not new to India but have recently come into everyone s attention due to certain high value banking frauds discovered in the past few months involving renowned business men and large banks. Image 1 below depicts a timeline on these frauds which have been caught this year. Recent High Value Banking Frauds Nirav Modi/ Mehul Choksi Dwarka Das Seth International Simbhaoli Sugars Kanishk Gold Multiple 12,636 Crore 390 Crore 109 Crore 824 Crore 750 Crore Punjab National Bank Oriental Bank of Commerce Oriental Bank of Commerce State Bank of India IDBI Bank Issuance of Fake LoCs / LoUs and Collusion of bank officials with perpetrators Using LoCs to pay off creditors against purchase of precious stones; fictitious transactions Diversion of loans for personal use Falsifying records and financial statements to get loans from the banks over a 10-year period Fraudulent loans for pisciculture and Kisan credit cards 26 Jan 19 feb Rotomac 24 Feb 26 Feb RP Infosystems 28 Feb 01 Mar 22 Mar 23 Mar Totem Infrastructure Subhiksha Trading Services Limited 29 Mar 30 Mar Sri Nangli Rice Mills 3,695 Crore 515 Crore 77 Crore 1,394 Crore 89 Crore Bank of Baroda Canara Bank Bank of Baroda Union Bank of India Bank of India Misappropriation of funds, criminal breach of trust and violation of FEMA Loans were taken on the basis of false and fabricated documents Fund Diversion Fund Diversion Bank sanction credit facilities on the basis of forged and fabricated stock statements, balance-sheets etc. Image 1: Recent High Value Banking Frauds FY17-18 has had frauds amounting to Rs 28,459 crores, the highest for any single financial year. The number of reported frauds has also been consistently increasing with 5,152 frauds in FY 17-18, all of them with a ticket side of at least INR 1 lakh. Chart 3 below depicts the Trends of Banking Frauds in India for the past five financial years. Chart 3: Trend Analysis Banking Frauds Measures by RBI The Reserve Bank of India (RBI) has given highest priority to addressing asset quality concerns as published in the Report on Trend and Progress of Banking in India Image 2 below gives an overview on various measures taken by RBI.

Involvency & Bankruptsy Code (BC) Revised Prompt Corrective Action (PCA) IND As 109 (IFRS) The IBC is a one stop solution for resolving insolvencies swiftly with a view to protect the interest of

4 Involvency & Bankruptsy Code (BC) Revised Prompt Corrective Action (PCA) IND As 109 (IFRS) The IBC is a one stop solution for resolving insolvencies swiftly with a view to protect the interest of small investors and promote easy of doing business Stricter PCA guidelines for all banks with a three-tier structure. Riskiness of a lender will be categorized as risk threshold 1, 2 and 3 with category 3 resulting into amalgamation, reconstruction & winding up Ind AS 109 will lead to early recognition and higher provisions for loans and off-balance sheet exposures using expected credit loss (ECL) model, thereby also impacting capital requirements Proposed Banking Regulation (Amendment) Act Resolution of Stressed Assets Revised Framework Differentiated Banking WLTF Banks Amendments were made to empower RBI to handle cases related to Stressed Assets. Central Govt. can direct RBI to issue proceedings against defaulters under the IBC Act Emphasis on identification of SMA accounts at early stage. Implementation of Resolution Plans to cure stressed assets. Wwithdrawal of all extant instructions such as SDR, S4A and CDR. High capital requirement Rs 1000 Cr. Would provide long term loans to corporates & would not be allowed to raise saving deposits from Individuals. Also act as market makers for corporate bonds, credit derivatives, warehouse receipts Image 2: Measures taken by RBI In terms of policy changes, the Insolvency & Bankruptcy Code (IBC) was introduced in 2016 followed by the Banking Regulation (Amendment) Act in 2017 to swiftly resolve stress in banking balance sheets. The revised Prompt Corrective Action (PCA) system was also introduced in April 2017 to pre-emptively recognize and resolve stressed assets. A high-level task force on Public Credit Registry (PCR) has also been set up which would review current availability of credit information, adequacy of current utilities & systems and instil global best practices in the banking system. RBI also published Resolution of Stressed Assets Revised Framework which now mandates lenders to identify defaulters at an early stage by classifying them as Special Mention Accounts (SMA). It also talks about implementation of Resolution Plan (RP), which would consist of board approved policies for resolution of stressed assets along with actions, plans, reorganization etc. including the timelines for resolution. Project Sashakt Acting upon the resolution plan published by RBI, the finance ministry recently launched a 5-point plan called Project Sashakt with the aim to resolve toxic loans. The 5-point plan would help retain the value of the asset through operational turnaround hence dismissing the speculations around creation of a bad bank plan. Under the plan, bad loans of up to INR 50 Cr would be managed by a focused team which would be internal to the bank ensuring that the loan is resolved within 90 days. For bad loans of INR Cr, banks will enter into an inter-creditor agreement, authorizing the lead bank to implement a resolution plan within 180 days which would include appointing turnaround specialists. If the lead bank does not complete the process in time, the asset would be transferred to the National Company Law Tribunal (NCLT). For Loans above INR 500 Cr, an independent asset management company(amc) would be setup supported by institutional funding in stressed assets or an alternative investment fund (AIF). The idea is to consolidate stressed assets under AMCs for faster and efficient decision making. Loans above INR 500 Cr would be transferred to the NCLT for resolution under IBC.

5 IFRS 9 (Ind AS 109) IFRS converged Indian accounting standard (Ind AS) for computations of expected credit loss (ECL), would be mandatory for banks from 1st April 2019, deferred by one year primarily due to the current stressed assets situation in the country. Ind AS 109 would require financial institutions to overhaul their current provision calculation and data aggregation systems and could eventually require higher provisioning capital. RBI is also promoting Differentiated Banking because of which Small Finance Banks and Payments Banks have already been setup and soon Wholesale & Long-term Finance (WLTF) banks would be seen. It is also expected that the regulatory frameworks for NBFCs, Financial Institutions and Cooperative banks would soon be aligned to that of commercial banks. Visualizing a System to Automate NPA Management With a renewed focus of RBI on NPA and frauds, financial institutions need to monitor asset quality very closely to ensure smooth functioning and spot any aberrations. FIs today have started adopting many predictive and pre-emptive strategies to improve asset quality to specifically minimize NPA levels. Predictive Analytics has been effectively used in some cases to score customers, based on behavioural and demographic /psychographic parameters. These scores are an accurate indicator of expected repayment behaviour and determine credit eligibility. Once assets are sold, pre-emptive analytics come into play. Early warning signals are created that flag off assets that are delinquent or tending towards delinquency. This provides a head start to Banks that then work with customers, to ensure they don t become full blown NPAs. In this manner, pre-emptive and predictive analytics play a key role in the NPA management strategy of a bank. Analysts and experts today consider NPAs to be one of the key indicators determining the health of the balance sheet along with other measures like CASA ratio and NIM. The regulatory guidelines, on the topic, classify assets into 4 broad categories standard, substandard, doubtful and loss assets with progressively increasing provisioning levels. There are also specific norms governing collateral apportionment, treatment of restructured assets and complex credit facilities like syndicated loans. Financial Institutions face many challenges on the path to achieve NPA automation and monitoring goals set by RBI. First and foremost, is the presence of multiple source systems, housing data pertaining to different asset products/facilities availed by the same customer. While, some of these systems may be equipped to automatically, perform NPA calculations, others are not. Secondly, RBI guidelines advice Banks to take a customer view rather than a facility view, which implies that data from multiple systems needs to be integrated to provide a unified NPA view at customer level. This is in tandem, with the cross-default clause, that features in many loan agreements and monitors repayment behaviour. Thirdly, the process of NPA computation needs to be automated to be able to handle different product types and the huge data volumes. Additional layers of complexity are also introduced by the presence of an underlying asset (collateral) whose value needs to be appropriately apportioned before calculating provisioning levels. Finally, a lot of information does not exist in the core systems but lies with the relationship managers (e.g. Customer current relationships/statuses with other banks, hypothecation of collaterals. Crop performance Agri loans etc.). Capturing the unavailable information into a system and using it for NPA identification is an important criterion, and should not be overlooked

6 Once key metrics pertaining to NPAs are calculated, they also need to be incorporated in the relevant financial statements. Additionally, a lot of analyses can also be performed at this stage to understand process issues, discrepancies etc. which led to NPAs in the first place. The insights obtained at this stage, would provide critical inputs, to the pre-emptive asset quality management strategies followed by Banks and go a long way in managing the challenge of balancing growth with quality. Overall, a key feature, any Bank will look for while automating NPA management is the flexibility to handle new product launches, new source systems and external changes like modifications in regulatory guidelines.an asset management cycle can be summarized as below: Preparation: This stage encompasses obtaining data from multiple source systems to ensure a single view of each customer. The challenge is also to handle and process a variety of special cases like securitized assets and syndicated loans. Early warning signals can also be configured at this stage based on which reports will be generated which lists out cases that fall into the potential NPA category. Processing: NPAs are first classified into the appropriate buckets after considering a variety of factors like vintage, product type, availability of collateral, product specific attributes (e.g. Agri Loans, Vehicle Loans, Credit Cards etc.). Calculating provisioning levels requires collateral data also to be integrated. Also, restructured loans have more stringent provisioning criteria which needs to be taken into consideration. There could also be some facilities which are exempt from NPA computation. Though NPA calculation and provisioning is automated, Banks will still probably need to cross check the calculations. In some cases, discretionary calls may also be taken to reclassify. At this stage, it is crucial to ensure changes are auditable which requires having in place a review mechanism. An accounting interface would also be needed, to seamlessly integrate the figures thus calculated, into the balance sheet, income statement and other relevant financial statements. Presentation: NPA and provisioning levels once calculated need to be reported. After meeting the obligatory regulatory requirements, Banks will also typically like to study the NPA data along multiple dimensions like product type, branch, geography and industry/sector and do a root cause analyses, to identify weak links in the asset lifecycle Smart Management of the asset lifecycle can enable Banks to not just be compliant, but over the long term, also help adjust their credit policy, product portfolio and lending processes in a bid to reduce bad loans. From a regulatory perspective, NPA data helps in building an accurate picture of asset quality which in turn becomes a useful input in macroeconomic policy making References 1. RBI - Financial Stability Report (Issue No. 17) June RBI - Report on trend and progress of banking in India December 2017

7 About Argus Argus is a one-of-a-kind leading provider of intelligence, decision support solutions, and advisory services to financial institutions across the global commerce ecosystem. Clients include more than 50 top U.S., Canadian, and other international financial organizations, regulators, payment providers, merchants, and media. Argus maximizes the value of data by transforming it into insightful information and analysis that assist clients in understanding their market contribution, managing and mitigating risk (default, fraud, funding, and compliance), and capitalizing on their financial objectives. Argus, a Verisk Analytics (Nasdaq:VRSK) business, is headquartered in White Plains, New York, with additional offices in San Francisco, São Paulo, Sydney, Melbourne, Bangalore, Mumbai, Dubai, Auckland, Singapore, and London. For more inforamtion, please visit For More Information Americas +44 (0) Europe and MENA Asia Pacific India argusinformation.com argus.communications@argusinformation.com

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