Combining enterprise and consumer credit bureau data to provide lean loans for small businesses

Transcription

1 Combining enterprise and consumer credit bureau data to provide lean loans for small businesses Dr. Frank Broeker SCHUFA Holding AG

2 Like in many other countries small & medium-sized enterprises are Germany s commercial backbone 76,1% German Federal Bureau of Statistics Share of the company population 14% 12% 10% 8% 6% 4% 2% 0% ,1% ,0% ,8% ,4% 0,7% 0,4% 0,1% 0,1% 0,1% Number of employees >2000 >90% of all German companies have less than 20 employees Its creditworthiness is mainly driven by its managers/ owners Hence, the usage of their consumer credit data is promising

3 Revenue and cost structures of loans for small & medium-sized enterprises are problematic Just 10 rating questions with 3 minutes each cost about 30 2 Special, financially unattractive exposures: founders of new businesses or undrawn facilities Each minute of internal workforce costs about 1 1 A loan of and 2% margin delivers just 200 return Average unit costs for developing and maintaining IT-rating systems are high Collect and work up external balance sheets costs about 50 each External enterprise reports and ratings cost a double-digit amount (in ) 1 Simplified full cost allocation 2 Ask rating questions, discuss and categorize answers, insert into IT-system or credit files

4 Drivers of lean loans for small businesses time is money Credit process automation offers process cost reduction by leaner and more standardised processes for small & medium-sized enterprises SME s increased customer satisfaction: applicants prefer fast und uncomplicated credit decisions forecast power is money Emphasising high predictive power of rating & scoring aims at a risk-based, individualised and fair pricing for SME s a reduced quote of rejected applicants (more green cases) more specific covenants for a narrow band of yellow cases reduced default rates

5 Scoring of small & medium-sized enterprises - process costs versus credit losses - For SME loans fast and cost-effective processes are at least as important as risk-sensitive obligor selection Client example: Annualised process costs and credit losses in % of the credit amount 2,8% Process costs p.a. Credit losses p.a. 2,1% 1,1% 1,2% 0,6% 0,6% 0,5% 0,4% Self-employed Small enterprises Medium-sized business Larger Enterprises... but often highly discriminative rating systems are one major precondition to simplify and accelerate loan processes.

6 Vision of a new target market: Turning small business loans into a lean banking product Loan approval process Differentiation of creditworthiness Loan granting workflows Monitoring obligations manual automated low high easy difficult laborious efficient Fully automated integration of ad-hoc available data into business processes Spec. scorecards with high forecast power for different target groups of the SME market Simple, reliable & already established interfaces to financial institutions Regular electronic update on data and scoring changes Intensified industrialisation of loan processes for SME clients comparable to consumer loan factories

7 Some characteristics of Germany s information market hamper the set-up of such a business model Scepticism from banks and financial institutions Relatively low numbers of business clients per bank hinder a lean loan approach (fixed costs problem) (in Germany: fragmented banking market with >2000 banks) Unstandardised processes with high individuality (banks) High complexity in the SME market (e.g. financial information vary in form, content, quality & reliability) The lack of publicly available, detailed and reliable data especially regarding small businesses Manual data gathering is expensive: -> Huge price differences between consumer and enterprise data Germany s 20:1 rule : - 65m adult consumers vs. 3,5m enterprises - price: enterprise credit report vs. consumer credit report

8 Non and near banks have successfully implemented lean loan business models Small ticket leasing e.g. IT leasing Supplier credit facilities Credit insurance companies SME lean loan business model (Very few) direct banks Truck, van, lorry & bus financing Company car financing and all rely heavily on credit bureau information

9 SCHUFA s road map to encourage lean SME loans Development of specific SCHUFA scorecards for sole trader enterprises, freelancers, and managers/owners based mainly on SCHUFA s own data Co-operation with Germany s second largest credit bureau for enterprise information Bürgel Wirtschaftsinformationen GmbH & Co. KG -> Full range of enterprise information data marketable via SCHUFA Set-up of SCHUFA Web as a web-based easy-to-use platform to access both SCHUFA as well as Bürgel credit reports and scores (extensibility to other credit bureaus is given by the IT architecture) Via Bürgel access to credit reports of international enterprises 5. Offering of integrated scores based on SCHUFA and Bürgel data. Integrating and optimizing information from different credit bureaus offers clients more simplicity and less IT, know-how & workflow efforts

10 SCHUFA BusinessLine Standard Scorecards Scorekarte small business (sole traders, companies with personal liability) Scorecard freelancer (pharmacists, doctors, architects, lawyers, ) Scorecard owner / manager (of limited corporations) development development cases development cases cases

11 Bürgel enterprise information are based on manual investigations and automated information collection Request triggered information sources Client Request independant information sources Telephone and personal interviews Public registers & directory lists Supplier feedback & interviews investigate Request Financial accounts/ balance sheets Public registers & directory lists Print & online media analyse BÜRGEL database collect Print & online media Payment experience & collections data Enterprise information

12 Basic scheme for an integrated SCHUFA-Bürgel credit report & score for small personal business Combined SCHUFA- Bürgel-Score Input mask Credit bureau request Check input data & request preparation DSS credit bureau connectors Plausibility: Check/filter the identification data DSS rules engine: data availability at the end of response time Standard SCHUFA BusinessLine Score Rescaled Bürgel Score Output mask Credit report here: SCHUFA as leading process not-identified back-up score (regional data etc.)

13 Basic scheme for an integrated SCHUFA-Bürgel credit report & score for limited corporations Input mask Credit bureau request Output mask Credit report Check input data & request preparation DSS credit bureau connectors Extract and prepare manager/owner data DSS credit bureau connectors Check data plausibility Adjust of Bürgel credit score based on SCHUFA data here: Bürgel as leading process

14 Standard BusinessLine Scores Freelancer scorecard statistics Portfolio share in % 25% 20% 15% 10% 5% Default rate in % 14% 12% 10% 8% 6% 4% 2% 0% A B C D E F G H I K L M SCHUFA score class / risk group 0% The 5% worst scores account for 25,8% of all future defaults

15 Relying on consumer and enterprise data sources increases the scorecard forecast power significantly Forecast power (Gini coefficient) for a small business portfolio of about enterprises used in scorecard development 61 % 54 % 42 % SCHUFA Standard consumer score applied on SME SCHUFA BusinessLine scores Combined SCHUFA/Bürgel scorecard To most clients forecast power is an abstract, hard to sell scorecard characteristic, for scoring/rating experts it is of central importance and even relevant for pricing. Integrated scorecards using client data atop achieve a still higher forecast power.

16 Financial impacts of increased forecast power Drivers and categories of the financial benefit Rating / scoring system Important characteristics: Rating process efficiency (especially driven by process automation) Discriminatory power (measured via Gini coefficient) Others like acceptance, coverage, calibration... Risk based Pricing? Reduced process time Improved obligor selection Non linear Basel II II formula Profitability Gross margin Process cost (staff & material) Expected loss (EL) Cost of capital Economic Value Added

17 Financial impacts of increased forecast power - Pragmatic rules of thumb - Each increase of the forecast power (Gini coefficient) by 1 percentage point reduces write-offs / credit losses of the portfolio by about 2% reduces the cost of capital of the portfolio by about 0.5 to 1% (based on Basel II calculations for banks, valid for economic capital as well)

18 Financial impacts of increased forecast power Example calculation regarding the Bürgel add-on Example: new instalment loans with an average exposure of 20T (-> 100m Euro) - Average PD till maturity: 3%, Loss Given Default : 50% (-> Exp. Loss: 1,5m ) - Basel II retail segment, cost of capital: 15% (pre-tax) - Increase in the Gini from 54% to 61% (Gini add-on by using Bürgel data) Rating / scoring system Important characteristics: Rating process efficiency (especially driven by process automation) Discriminatory power (measured via Gini coefficient) Others like acceptance, coverage, calibration... Risk based Pricing? Reduced process time Improved obligor selection Non linear Basel II II formula Profitability Gross margin Process cost (staff & material) Expected loss (EL) Delta: 228 T (22bp) Cost of capital Delta: 75 T (8bp) Economic Value Added Delta: 303 T (30bp) Savings per Score 45,65 14,92 60,57

19 My personal summary Lean loans for SME s are a growth market (worldwide) Scepticism from lenders as well as process obstacles still hamper a large scale usage of fast credit workflows based on credit bureau data There a few rationales to be found why a score-based approach with credit bureau data at its centre (that has proven to be a success story in the consumer credit market) will not take off for SME s as well A combination of consumer and enterprise credit bureau information may offer the potential for a breakthrough for lean SME loans Co-operation with competitors, different business & process models as well as pricing & legal structures are management challenges Short process time, high forecast power as well as high reliability and skilled sales & marketing forces to sell abstract and intangible credit scores are key drivers for a credit bureau success in this market

20 Your contact person Dr. Frank Broeker Head of Rating Services SCHUFA BusinessLine Bremen Hamburg Hannover Berlin SCHUFA HOLDING AG Kormoranweg Wiesbaden Germany Phone +49 (0) Fax +49 (0) Bochum Düsseldorf Köln Wiesbaden Frankfurt Mannheim Saarbrücken Stuttgart Leipzig frank.broeker@schufa.de München