Analytics Revolution - Why the struggle for growth?

IT majors have been excited about the convergence of Social, Cloud, Analytics and Mobility (SCAM).  It is widely believed that these will be the engines of growth in the future. Rightly so. N Chandrasekaran, CEO of TCS, India’s largest technology services provider, recently referred to the SCAM as "digital forces" and estimates that these digital forces would be a $3-5 billion opportunity in the next few years.  A Gartner study has reported that the SCAM market will be worth $107 billion by 2017.

It is true that Analytics - has generated excitement all around. Everyone can see and experience the impact that this convergence – that engenders Disruptive Innovations  - has on everyone’s life. I personally think that the hype is real and the huge revenue opportunity projected for this market space is based on solid grounds.

What the TCS Chief has not mentioned is that there are significant white spaces – industry-speak for critical gaps and blind spots in the effort to get this revenue. And the fumble, too, is very real. For example, if these projections and forecasts can be translated into revenue, why are we not seeing a Google or a Facebook or even their dwarfs in pure play Analytics?  There appear to be several reasons why IT majors have not been able to take advantage of the opportunities. The revenue is for them to lose unless they learn and take corrective action quickly.

Analytics business is a domain specific, hands-on and a devilish details game where domain expertise is all supreme. However, most global players have not been able to get the right folks to lead the practice. This has proved to be a disastrous non-starter. The problem is also compounded by lack of right skills in the marketplace. The analytics practices at the majors continue to be led by professionals who either have consulting or technology background but weak in hands-on analytics. This has blissfully insulated the practice from the analytic humdrum that businesses are experiencing. This is also reflected in the inability to identify or devise the right vehicle to exploit the surging analytic opportunities. In my view, the lack of appropriate leadership is a major roadblock to growth.

The IT majors also urgently need to revisit the internal business structure. The bunching of analytics catering to different industry segments or verticals under a single business unit may be convenient for administrative and bureaucratic reasons, but has not produced optimal results. This agglutination has come in the way of insight dominance since successful thought leadership in one vertical often has not passed muster at another. I think the analytics practice catering to each industry vertical must be a separate business unit by itself.

The outsourcing industry has mastered the art of building the business via the IT organizations of client companies. However this tested path has not helped build the Analytics business because the key players are not on the IT organization of clients. Outsources need to have a game plan for directly engaging the business side of the house.

Further the majors they are selling software products and tools that are often peripheral and non-core to generating analytical insights. Aided by an expanded definition of analytics, this may help generate revenue in the short run, but this has taken the focus off the insights business.  For example, a hypothetical solution that can build and deliver fraud detection models using large attribute set – including social media attributes – and look-up more than 10,000 datasets and yet instantly deliver accurate detections will be immensely popular.

Big data or new modeling techniques by themselves would not produce a disruptive innovation. The marriage of cutting edge technology and the resulting new innovative analytical techniques that can scale is the winning recipe. This is a keystone for success in analytics practice, yet conspicuous by its absence.

This success recipe has to be combined with a smart go to market strategy. I call it winning-with-a-thousand-cuts strategy. Instead of waiting for the dream multi-million, multi-year project, the focus must shift to building volumes through a huge portfolio of mid-sized projects. Execute several small to medium sized projects that will provide insights to the businesses in short to medium term - 6 to 12 month time frame. This paradigm has the potential for depth - to open up opportunities in every line of business, business unit or team level at clients and hence build scale in the analytics business.

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.