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Wednesday, June 22, 2011

Enhancing Social Network Intelligence



I was browsing the internet to see how companies have come up with several strategies to make their social media presence felt. I had already written about how social networks are beings used by debt collection companies to track delinquent customers.

My insurance company (auto & home) is pestering me to add it as a friend on Face book – which is really annoying.  There are definitely better, more subtle less annoying ways to befriend the right customer or prospect.  I want to discuss one such methodology.

We can extract useful insights from social media data by combining it with what we already know about the customer from other databases such as credit bureau, rewards and loyalty programs, public records, credit card authorization data, etc.  A smart coupling of any of these databases with social media data enhances quality of social network intelligence (SNI).

Let me walk you through a hypothetical example to elaborate my viewpoint.  Mr John is a Marriott rewards member with a Marriott rewards credit card. He has accumulated significant reward points over the years.  He is active on Facebook where his posts include several dinner outings he has enjoyed with his significant other. Let us examine how we can convert this social media post into actionable intelligence or Social Network Intelligence (SNI).

Data on John’s stay at the Marriott as well as rewards card usage is captured by Marriott and Chase Cards and mutually shared.  When we combine transaction level data i.e. card authorization data with John’s Facebook posts, we have an intelligence multiplier. We can authenticate or validate his favorite cuisines by cross matching social media posts with credit card authorization data with the following information:

a)    how many times in a given period did John eat out
b)    how much did he spend 
c)    the restaurant  and (hence the cuisine) he frequents

Here two important points have to be noted

1)    Social Media posts is tracked for actual willingness and the ability to pay for choice of product  / service – cuisine in our example
2)    By this validation , we have converted a social media posts into social network intelligence -  euphemism for  actionable intelligence

Social networks provide a wealth of information, but often in its raw data. For example John’s posting about his favorite dinner outings viewed in isolation may not be as powerful as when we know if he has actually spent money and how much on his dinners.  When combined with other databases, social media posts enhance its commercial usability.

Wednesday, June 8, 2011

Fee Income – Different Strokes by different Banks


I just read that the Senate voted let the Federal Reserve limit debit card swipe fees to $0.12 average per transaction, down from $0.44 average per transaction.  

The debit card fee has been a contentious issue that witnessed lobbying by retailers/ merchants and counter lobbying by Banks. Today’s news is a victory for merchants in a long-running fight with banks.

According to the Oliver Wyman report published earlier the Fed’s proposal could trigger a 73% decline in financial firms' fee revenue, from $16.2 billion in 2009 to $4.4 billion. 

No wonder the issue was contentious.

While banks and credit card companies have been losing fee income in post crisis regulations, this Senate vote has only added to their woes.

The innate strength of the banking and financial system has always been its ability to innovate and find new solutions to its challenges. How have Banks and financial services companies responded to loss of fee income?  

Well, different strokes by different banks.

Banks have responded to reduced fee income in different ways.  For example earlier this year, Bank of America introduced four new accounts where users pay fees unless they keep minimum balances, make regular deposits, use credit cards or take advantage of online services. I would definitely not call this innovation!

But I think the best innovation in fee income that I liked came from M&T Bank.  The bank recently announced the launch of new tools for customers to manage all finances and see their credit scores from a single screen. Finance Works enables account holders to view all financial accounts - credit cards, loans, checking, savings and retirement accounts while Credit Score for a monthly fee of $2.99 enables the account holder to see their credit score, refreshed on a monthly basis. These service offerings represent new and creative way to engage the customer, while one service brings in fee income.

While it is true that banks are facing pressure on fee income due to new regulations such as the new debit card fees limit imposed by the Fed,  smart players are leading the way by innovation. 

Greater the challenge, greater the innovation. So I definitely expect to see more innovative service offerings from banks and financial services in the coming months.

Tuesday, June 7, 2011

Google Wallet – The Industry Changing App



Google recently announced the launch of Google Wallet, the new point of sale payment app.  This cool Android app was launched by Google together with Citi Bank, MasterCard, First Data, Sprint  and some big name retailers.  This app is all set to revolutionize the dizzy world of payment technologies. 

No more bulky wallets with so many cards – the debit card, the reward cards, the airline cards ; You can also use coupons and other discounts at check out.  Wow, what a relief to have all these rolled into one app on your cell phone.  

Right now, the app will run on Android only.  However, given the size of the payments business, it is not hard to foresee that Google will enable the app on iPhones and Blackberry in the coming months.

According to Google, the Wallet app incorporates sever security features including PIN requirement before payment as well as the encryption of MasterCard’s PayPass technology.  Industry pundits   seem to have different views on this. But that’s beside the point.   How will this impact the payment industry? 

Ø  Definitely it is going to smoothen customers’ shopping and payment experience.  Paying at the checkout will never be the same again. I would expect the younger population segments to adapt quicker to this mobile payment technology. The older generation and baby boomers may not be far behind, though.

Ø  The credit card majors are nervously watching this development.  I think Google is just a step away from launching its own card business, or shall we say mobile payment business.  Banks get a share of the revenue Retailers generate from their rewards programs. This revenue share or at least parts of it for a start could now be up for grabs.  It will certainly eat into the profit margins in an already troubled credit card industry. 

Ø  Google Wallet has the potential to realign payment industry. The coming months should see clearer battle lines drawn between Google and friends on one side and the big boys of banking on the other side. They will be fighting for the consumer’s wallet share. Consequence - Most banks will be forced to invest in mobile technology to protect their customer base and market share and will offer huge opportunities for MicroStrategy on a platter

Ø  Will Google Wallet cause the disappearance of the plastic card? Don’t think so. While it is too early to predict how this will impact the plastics, I certainly think that the usage will start declining. I don’t think it would disappear altogether in the near future.  Well, you never know and I could be horribly off the mark.

Risk Data Silos


In an article published in Future Banking the authors have lucidly presented a  case for using Business Intelligence to manage the chaotic world of data silos. They have made specific reference to risk data silos in the context of Basel III. According to Chartis, Banks and financial institutions worldwide will invest $27 billion in their risk management initiatives – that will include tools to dig into these silos - to provide actionable risk intelligence. These initiatives will form the crux of the post crisis response to a new regulatory regime as well as information consumption paradigms that are emerging.  

When we look deeper into these extant data silos, we definitely see more chaos than order even in large financial institutions.  In fact I would argue that the chaos is independent of the size or market share of the bank. 

The center piece of credit risk management is customer intelligence.  From a bank’s perspective, knowing the customer – beyond getting the contact address and drivers license info – is key to managing its default risk. 
Using their customer insights, Banks typically cross sell a variety of loan products   - from home equity lines to credit cards -to existing DDA customers.   

Below are some examples that I have seen in my experience while working at large banks.

a) the credit card SBU would be offering  lower credit line to a high net worth customer resulting in poor customer experience and potential loss of business
b) the loan officer would be happily underwriting a mortgage loan to a customer who  currently has past due payments on Credit card
b) the credit card SBU would  continue to send mail solicitation to its retail banking customer who has just started experience difficulties in making monthly mortgage payments
( Note: Much of this intelligence cannot be garnered from credit bureau data because there is a time lag before the bureau reports it)

All these could have been easily avoided if the bankers had timely and complete vision of truth or appropriate business intelligence.

The recent financial crisis spawned a massive loss mitigation effort by the banks. Many creative strategies have been developed to proactively manage default risk. For example, retail banking relationship – defined as having a checking account or a credit relationship (example – auto loan) provides powerful insights and often early warnings on loan performance.  Intelligence on presence or absence of significant retail relationship is now a key component of a bank’s new account acquiring strategy. However, the existence of data silos is a hindrance to providing a 360o view of the customer.  

Interestingly, banks have the analytical capability to compute the benefits or loss savings that will accrue if they remedy the situation .  In other words they know in dollar terms how much this intelligence costs them.
Another dimension that forces attention on the chaos is the new regulatory regime such as Basel III. Compliance to the new regulations would automatically mean better data management leading to timely risk intelligence. Banks will need additional investments in time and money for compliance.
  
These insulated data warehouses and silos often weaken the integrity of a comprehensive credit risk management system.

Risk Intelligence from Social Networking – New Dimensions in understanding Consumer behavior


 
I came across a recent news item in Orlando Sentinel about how a Florida judge stopped a debt collections agency from pursuing an auto loan customer who was late on payments. Apparently the collections agency tracked the customer’s Facebook account and contacted her as well as sent messages to all her friends on Facebook. The judge’s ruling in the case may have highlighted privacy concerns, but what is of interest is that social network intelligence are opening up new and creative ways of understanding and managing consumer behavior.

Now, if debt collections agencies can use social networks in recovering payments - well at least until the Florida judge’s ruling – it would be of interest to know how these networks can be used in larger credit risk management initiatives by banks and financial institutions. While the regulatory / compliance / privacy angle is evolving, there is no doubt that social network intelligence provides powerful yet undiscovered dimensions to our understanding of consumer behavior.

Banks and financial services companies have always sought to better understand their customers. Their primary resource has been the credit bureaus (Experian, TransUnion, Equifax) who have a comprehensive record of credit activity of consumers.  From marketing to risk management, the credit bureaus continue to be the single go-to place for insights on customer’s past and also predicting future behavior.  Using their vast store of data, the credit bureaus created credit scores that were designed to predict the probability of a credit outcome such as default on a loan. 

Conventional risk analytical paradigms overlay credit bureau data on set of existing customers whose performance (example payment behavior) is fully tracked internally.  This would provide insights on external behavior - such as payments as agreed to other lenders or shopping for more credit - that may be useful. Matching this insight with internal data would provide risk intelligence that would be used in granting or denying additional loans / lines to the existing customers.

But what type of risk intelligence can be generated using social network intelligence?  While this is unchartered territory, definitely we can identify social network behavior of customers whose payment behavior we already know. We can use the same analytical paradigm of taking a set of existing customers and match it with social networking data – travels (location), payment transactions to glean insights. For example we can track delinquent customers’ travels and see if their delayed payments arise from travel and travel related splurges.  Frequent holiday travel alerts emanating from social network intelligence can lead lenders to proactively minimize / reduce / freeze credit lines. While the legal and regulatory ramifications of such actions are evolving and will be tested in courts or when regulators take a stand, it is well known that Banks have used and will not hesitate to use customer intelligence in creative ways to manage their profitability.

I see a new dimension to credit risk management opening up by using social network intelligence. There is no stopping this genie.

Retail Banking Risk Dashboards



This write-up details KPIs that provide Risk Intelligence on a wide spectrum of consumer lending products. These reports provide actionable intelligence from executive management to operational managers in Banks and financial services institutions. These reports can be used to report on a host of loan products such as

o   Home equity lines of credit (HELOC)
o   Home refinance loans (HRL)
o   Home mortgage
o   Auto loans
o   Credit Cards
o   Unsecured loans
o   Small business loans (SBA) / lines of credit

Further these reports can easily be tailored to report on other unique or exotic consumer loan products offered by individual Banks and financial services providers. Currently most bank use Excel or PowerPoint paper outputs as the delivery media for these reports. Since usually these reports make a big pile of paper reports, they provide a great opportunity for  iPad apps.

Retail Banking Dashboards that support loan products management generally fall under two large groups of reports. The groupings reflect the back-end databases/ source systems from which these dashboards are generated. 

o   Acquisition Related Reports
o   Existing Account Reports

In addition to these reports, Banks also invest resources in building Regulatory / Compliance / Governance / Audit reports.

Acquisition reports are really a point in time view of an ever moving target. For example it provides executives and managers insights on day-to-day account bookings, dollar volume, demographics and risk segmentation of credit qualified prospects and approvals. They provide snapshots on short time spans typically daily or weekly reports. They typically use OLTP / front end underwriting systems as data source to generate the reports.

 On the other hand existing account/ portfolio reports generally source their data from a data warehouse that is refreshed usually every month. In large banks, typically several intelligent cubes or OLAP cubes will be the prime provider of data for such reports.  Although there is a time lag, these reports provide powerful insights to the Chief Credit Officer / Chief Risk Officer and his executive team to efficiently manage their portfolio in accordance with stated corporate objectives - usually financial targets.

Risk Management KPI for Consumer Lending Portfolio - An illustrative list of reports

Acquisition Related Reports

ü  New Account Acquisition Management KPI
ü  Loan applications volume / on line enquiries / pre approved solicitations
ü  Approval Rate  i.e  Approved Applications / Credit Qualified Population
ü  New Customers vs new applications from existing customers – Wallet share?
ü  Customer Segmentation -   Risk Segments  - By Credit Score Bands
ü  Income segments  -  Debt to Income ratio
ü  Current Credit  Bureau Debt Level  - shows overall debt burden
ü  Existing customer – Wallet Share?
ü  Geographic Segments                   
ü  Average  Daily Balance on DDA
ü  Exception Reporting  Low Side Overrides/ High Side Overrides
                        
Existing Account Reports / KPI

ü  Balance and account attrition reports             
ü  Current level of Line Utilization or unpaid balance
ü  New loans sought in last 3 months?  Shopping or Wallet share opportunity?
ü  Credit Bureau Triggers - Any derogs  with any other lender?
ü  Debt to income ratio changes / alerts
ü  Change in income segments – Mass affluence migrations/changes
ü  Credit Score Migration (Change / Deterioration in loan quality)
ü  Adverse public data – possible impact on loan quality
ü  First Payment Defaults
ü  Delinquency Reports / KPI – Past due reports by severity
ü  Charge off reports – Loan default KPIs
ü  Bankruptcy  Loss Reports
ü  Fraud Losses
ü  Forecast vs actual loss reports

As mentioned earlier, this is only an illustrative list to provide guidance in designing Risk Intelligence Dashboards for Retail Banks and Financial Services. Different variations and permutations of these reports can be incrementally added to meet client’s requirements.

Credit Risk Primer

Credit Risk Management has attracted a lot of attention in the recent times. The recent financial crisis has been a trigger for this new interest. The discipline itself, however, is as old as banking and money lending. In this paper, discussion will be focused on the credit risk management function in retail banking and the credit card world.
After the recent financial crisis, banks are investing heavily in strengthening their risk infrastructure by bringing in new technologies and tools like Business Intelligence (BI) Tools. These new tools will enhance timely delivery of key risk metrics that will help risk managers mitigate portfolio losses. 

A commonly found definition for Credit Risk is that it is the risk of loss due to non payment on loans as agreed. When the borrower is unwilling or unable to adhere to the repayment schedule, the loan account becomes delinquent and is eventually charged off if no further payments are received. This is a real risk that directly impacts the lender’s bottom line. Identifying, quantifying and mitigating this risk of loss of capital and interest are the principal focus of Credit Risk (CR) Management , the subject of this primer. 

CR is a key function in all banking and lending institutions - whether they are small, medium, or global financial institutions. It plays a pivotal role by designing the Bank’s loss management and regulatory compliance policies. The degree of sophistication, however, of the risk function varies across these organizations.
Organizationally, individual banks may have different structures for CR. Two common arrangements are generally found: In one, multiple responsibilities making up the CR role report to a single function or strategic unit.; In the second, CR reports to the Chief Credit Officer and the functions are often named differently.

Credit Risk Management Function
CRM functions in a bank typically include the following responsibilities:
Ø Credit Risk Policy
· New account acquiring system
· Portfolio Risk management (existing loan portfolio)
Ø Loss Forecasts
Ø Decision Sciences and Predictive analytics
Ø Collections
Ø Risk IT
Ø Risk Governance , Compliance and Audit

Credit Risk Policy: This group is responsible for design, testing and approval of the bank’s credit risk policies. Policies include new account acquiring policy, portfolio risk management policy, fraud risk management policy, etc. For example, new account risk policy will determine the cut-off credit score above which new accounts / loans will be solicited. These decisions are supported by detailed and in-depth analytics on customer data that is often supplemented with additional data purchased from credit bureau or other external vendors. On the other hand, for loss mitigation on existing portfolio, customer retention strategies would constitute portfolio risk management policies.

Loss Forecasts: Banks compute Allowance for Loan and Lease Losses (ALLL) forecasts to provide for loss reserves. While several methodologies are available for computing loss estimates, the portfolio delinquency and flow rate methodology is widely used. This method uses loan past due information and rate of worsening delinquency to forecast losses. Many banks also use macroeconomic indicators / metric overlays like unemployment rate to fine tune their forecasts. Adequate loss reserves safeguard the bank as well as the depositors against expected losses in loan portfolio. Federal banking regulators such as Federal Reserve, Office of the Comptroller of the Currency (OCC), Federal Deposit Insurance Corporation (FDIC) and other Bank supervisory /regulatory agencies have issued detailed guidelines on loss reserves for banks. 

Decision Sciences and Predictive Analytics: This group uses statistical and data mining techniques to review historical customer data to identify behavior patterns and predict future customer behavior. This group custom builds scoring models that are targeted to predict given outcomes. Smaller banks buy scoring models from credit bureaus or other vendors. 

Collections: Often known as the Customer Support group, this group designs collection policies and strategies to recover receivables and past due payments from delinquent customers. 

Risk IT is outside of the Information Systems (variously known as IS or IT in many financial institutions) and generally reports to the risk business leadership. They are charged with implementing / executing risk policies on the different platforms and IT infrastructure in use. 

Risk Governance, Compliance and Audit group provides management oversight to CRM and ensures that all regulatory policies are in compliance.

Role of Data Analytics
Data analytics provides an important role in CR management. These can be regular or ad hoc analytics designed to provide specific information e.g., loan portfolio segments or customer responses to solicitations. Typically, large volumes of data are processed to segment the portfolio and compute key risk metrics. Banks usually use SAS, Cognos, Business Objects or MicroStrategy as their analytical toolset. 

Risk Reports, Dashboards & KPIs play an important role in Credit Risk Management. Reports such as those which show 30-Day, 60-Day and 90-Day delinquent accounts provide quick insights into the state of the portfolio. Segmentation of delinquency by customer profiles like risk-score, household-income and credit-utilization bands quickly deepen Management’s understanding of portfolio performance. Many banks have separate group within Credit Risk Department dedicated to producing risk reports.