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.

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.