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NPA Management

The Need To Balance Credit Quality With Growth

The Financial CrisisThe 2008 financial crisis was a turning point in the history of the financial world for many reasons, the most important being the heightened awareness and concerns around asset quality. Every step of the lending process, from customer verification, profile risk assessment to collateral valuation came under the scanner. The analysis concluded that there exists a trade-off between asset growth and quality, and in the quest for growth, quality had been compromised.

The India StoryIndian Banks today are in a phase of rapid growth, with a credit to deposit ratio of 75%1 and an average annual credit growth of 15%2. Banks are faced with the challenging task of maintaining/increasing credit off-take to fuel GDP growth, while also ensuring quality is not compromised. An obvious outcome of low asset quality is the growth in Non-Performing Assets (NPAs). NPA growth needs to be supported with a higher level of provisioning, which in turn has a direct impact on bottom-line. Banks see a definite drop in retained earnings and are forced to raise more tier 1 capital to sustain the same level of credit disbursements. Intuitively, this points to an inefficient utilization of resources with ratios like profit margin and RoE being directly impacted.

Concerns around asset quality have been a focus area for the Indian regulator as well. The Reserve Bank of India (RBI) has talked about “systems to track and classify NPAs” way back in 2008. The RBI Master circular dated September 2012 details out the Prudential norms on Income Recognition & Asset Classification (IRAC) and emphasizes on the need for an effective mechanism and granular level data monitoring for NPA. The finance ministry also echoed this sentiment when it directed all Public Sector Unit Banks (PSU) to automate calculation and monitoring of NPAs by September 2011, and later extended the deadline to March 2012. The basic objective is to increase transparency and consistency in financial reporting while creating an effective mechanism to monitor NPAs.

In early 2013, Moody’s had downgraded the rating of the Indian Sovereign and Banking industry from “Stable” to “Negative”. Key reasons for this were the rising NPA levels and quantum of restructured loans both of which were considered, symptoms of deteriorating asset quality. Some other studies, also predicted that NPA levels for Banks were expected to reach 4% by March 2013 and 4.4% by March 2014. Overall, the current NPA status was grim and checks and balances to monitor asset quality and NPAs in particular were the need of the hour.

The NPA data below shows trends across Indian Banks. While Gross NPA gives an overview of gross NPAs/Gross advances, Net NPAs are calculated after taking into account the provisioning already in place. NPAs peaked in 2008, post which there has been a gradual decline. However, for PSU Banks which have a greater exposure to risky sectors like power and agriculture, an increasing trend can be observed. Private sector and foreign Banks appear to have been fairly successful in controlling NPAs.

NPA Management. The Need To Balance Credit Quality With Growth

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1 RBI - Statement 1: Commercial Banks at a glance - September 2012 2 YOY growth rate as on December 20123 RBI report on Trends & Progress of Banking in India 2011-12

NPA ManagementThe Need To Balance Credit Quality With Growth

Gross Advances Gross NPAs Gross NPA % Net Advances Net NPAs Net NPA%

2008 25,079 564 2.3 24,770 247 1

2009 30,383 682 2.3 30,009 314 1.1

2010 35449 847 2.39 35013 391 1.12

2011 43511 979 2.5 43106 418 0.97

2012 51589 1423 3.1 50842 649 1.4

3

2.2 2 2.2 2.4 3.3 2.3 2.4 2.3 1.9 1.8 2.2 3.1 2.9 2.7 2.2 1.8

4 4.3 2.5 2.6

FY 2007-08 FY 2008-09 FY 2009-10 FY 2010-11 FY 2011-12

Gross NPA to Gross Advances %

Foreign Banks

New Private Sector Banks

Old Private Sector Banks

Public Sector Banks

1 0.9 1.09 1.2

1.7

0.7 0.9 0.8

0.5 0.6

1.2 1.4

1

0.6 0.5 0.8

1.8 1.9

0.6 0.6

FY 2007-08 FY 2008-09 FY 2009-10 FY 2010-11 FY 2011-12

Net NPA to Net Advances %

Public Sector Banks

Old Private Sector Banks

New Private Sector Banks

Foreign Banks

In this era of global connectivity and quick transmission of shocks, banks need to monitor asset quality very closely to ensure smooth functioning and spot any aberrations. Banks today have started adopting many predictive and preemptive strategies to improve asset quality to specifically minimize NPA levels. The prerequisite for this is to monitor the entire asset life cycle very closely.

NPA ManagementPredictive Analytics has been effectively used in some cases to score customers, based on behavioral and demographic /psychographic parameters. These scores are considered to be a fairly accurate indicator of expected repayment behavior and determine credit eligibility. This can be used effectively to decide whether or not an asset product is to be cross-sold to a customer or to determine the terms. Credit Information Bureau India Limited’s (CIBIL) scores, attempt to provide this kind of data even at an inter-bank level.

Once assets are sold, preemptive 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, preemptive 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.

Banks 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. 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 behavior. Default on one facility will have consequences on all the other facilities granted to the customer by the same Bank.

NPA Management. The Need To Balance Credit Quality With Growth

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• Historical data to model customer behaviour

• Credit Scores provided by Third party

Predictive

• Early warning signals

• Restructuring / Special Offers when delinquent to prempt NPAs

Preemptive

•Various instruments - bills, OD /CC, Term Loan, Consortium Loans, other special cases

•Buckets NPA

Classification

Provisioning

Income Recognition

Standard Asset- 0.25%- 1% of outstanding

Sub Standard Asset -15%

Doubtful -100% (unsecured)+ 25%-

100% (secured)

Loss Assets – Write off/ 100% (outstanding)

Standard <90 days

Substandard 90-365 Days Doubtful Loss

NPA Classification

• Standard <90 days

• Substandard 90-365 Days

• Doubtful

• Loss

Provisioning • Standard Asset

0.25% - 1% of outstanding

• Sub Standard Asset 15%

• Doubtful 100% (unsecured) + 25% - 100% (secured)

• Loss Assets Write off / 100% (outstanding)

Income Recognition

NPA Management. The Need To Balance Credit Quality With Growth

Secondly, 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. Automation promotes consistency and a relatively error-free calculation while potentially reducing resource requirements for the activity.

Finally, once the 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, which led to NPAs in the first place. The insights obtained at this stage, would provide critical inputs, to the preemptive 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 is depicted in the following diagram.

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 on the basis of which reports will be generated which list out cases that fall into the potential NPA category. This is detailed in an earlier section of this paper as a typical preemptive step to ensure assets do not become NPAs.

Processing: NPAs are first classified into the appropriate buckets after considering a variety of factors like vintage, product type, availability of collateral. Calculating provisioning levels requires collateral data also to be integrated. Also, restructured loans have more stringent provisioning criteria which need 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 ensure changes are auditable and this 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.

ConclusionSmart 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 <

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Monitor

Early warning flags

Automate NPA Computation

Handle a variety of asset classes. Provide unified view of NPAs

Provision

Built-in accounting interface

Meet Regulatory Requirements

Regulatory Reporting

Multi-dimensional Analyses

Insights into potential causes for NPAs

Preparation Processing Presentation

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About the Author:

Shivani Venkatesh is a Lead Consultant at iCreate and comes with rich experience in Consumer Banking (Channel Management, Proposition Development, Customer Portfolio Management and Segment Strategy). Shivani is currently working with iCreate’s solutions team in building nextgen banking decision enablement products and solutions.