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28 January 2014 Good Hope Chamber Cape Town.  Who & what the Credit Providers Association is  Why data matters so much  How the world of credit data.

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Presentation on theme: "28 January 2014 Good Hope Chamber Cape Town.  Who & what the Credit Providers Association is  Why data matters so much  How the world of credit data."— Presentation transcript:

1 28 January 2014 Good Hope Chamber Cape Town

2  Who & what the Credit Providers Association is  Why data matters so much  How the world of credit data in SA currently operates  The concerns about the NCA Amendment Bill proposals  The intention of today’s presentation

3 “Data is to financial well-being as what water is to life”

4  Voluntary Association of industry players who share payment performance information about their debtors with the aim of making the best credit and risk decisions possible  Non profit Association  Name change in progress  Association Participants*: ◦ NCR registered credit providers ◦ Non-NCR registered providers – e.g. utilities, subscription services service providers ◦ Service providers to full members for data analyses ◦ Credit reporting bureaus

5  Forum for credit information exchange, but… ◦ Very limited information exchange compared to what is allowed to be reported to credit bureaus within Section 70 of the NCA. In fact only the payment performance and also demographics for purposes of identifying consumers is shared through the CPA forum. ◦ Not without consumer consent. ◦ Not without some existing knowledge of the consumer. ◦ Only within the permissible purposes and allowances afforded within the National Credit Act, e.g.:  credit vetting,  employment vetting at time of application for a job where trust and management of cash or finances is required,  fraud investigation,  consumer reviewing his/her own credit profile  debtors book evaluations etc.

6 FactorFeatures Specified data layout: Data reporting frequencies:  Mandatory and optional data fields  Positive and negative data =“gold/black list concept”  Common “language” through product codes and account performance status codes  Consumer & in the future business payment performance data  Monthly for full debtors books and soon within 48 hours for credit extended and accounts closed

7 FactorsFeatures Reciprocation principles: Submissions via agreed transmission mechanisms:– (i) “The Hub” - portal (ii) Dedicated data lines between members and bureaus for split second responses (iii) Web enabled graphic user interfaces for online enquiries  Authorised access  Membership categories  “Like for like”  Censorship through exclusion where data is not to standard  Easily controlled and secure transmission of data to 4 hosting bureaus  Return strings of data as negotiated by members with bureaus according to members’ needs and relative to bureaus products offered to their clients

8 FactorsFeatures Agreed data quality standards: Standard operating procedures (SOPS):  Exception reporting – bureaus to members and the Association  Acceptable norms and deviations established and managed  Remedial actions with deadlines  Triggers and alerts on problems for mitigating risks in both data and business decisions  Commonality allows for easy adoption and compliance  Rapid identification of deviations  Highlights weaknesses and opportunities for data quality enhancements  Promotes legal compliance in terms of data management and reporting practices

9 FactorsFeatures Constitution and Code of Conduct: Clarity drives accountability:  Defines roles and responsibilities  Drives accountability  Allows for effective allocation of Association resources  Informs priorities for all  Enhances management of Association members by CPA staff – small complement  Defines financial contributions to the Association  Defines recourse options & sanctions for failure to comply  Increases protection for the consumer  Champions the data cause!


11  Insights into consumer behaviours for business risk assessments and risk mitigation strategies and tactics  “Thin” file consumers (young and early entrants) gain access to credit which they would otherwise not  Affords financial inclusion of more people into the formal economy where consumer protection exists  SMME’s are able to access and extend credit, this benefits themselves, their customers and contributes to employment

12 Market optimisation, risk mitigation, customer satisfaction & value

13  Credit vetting: ◦ Application stage and on-going for facility changes  Propensity models: ◦ Lapse, early settlement, facility changes, pay/no pay, payment, default rate  Tracing  Fraud identification and prevention  Marketing – supplementing marketing lists  Relationship management  Debt consolidation  Triggers & alerts  Employment vetting  Development of risk assessment tools: ◦ Research and analysis  Price/premium setting

14 Then …  In the 80s with periodic meetings and a few words about customers payment performance scribbled in note books and spoken of in hushed tones! Now …  A flow of payment profile lines on 71.17 million accounts per month!  NCA enforced data attrition - daily! Currently amongst the world’s best credit reporting systems! (World Bank – “Ease of Doing Business” survey)

15  Business grows  Consumers have options  Government receives more taxes  statistical insights exist to inform all…  ……..Markets grow! CategoryMar. 2011 quarter March 2012 quarter March 2013 quarter September 2013 quarter Consumers 18,60m 19,49m 20,08m20.29m Accounts 63.05m 67,51m 70,73m71.17m Enquiries 176,65m 310,42m 394,47m357.65m Source: Credit Bureau Monitor ex NCR - Quarterly reports

16 Is not where consumer financial education is at yet….. Information CategoryMarch 2012March 2013September 2013 Profiles issued to consumers120 094145 720164 437 Disputes lodged10 35716 86821 466 Resolved in consumers’ favour 7 72212 31315 731 Info unchanged1 6463 6704 064 Source: NCR Credit Bureau Monitor Quarterly reporting

17  The volume of global business information doubles every 1,2 years!  Poor data costs 20 -35% in operating revenue  Poor data quality costs US businesses $600 billion p.a. And the US economy $3,1 billion annually!

18  More inclusive market with wider access to credit  Influences the cost of credit to suppliers and users/consumers  Contributes significantly to regulatory compliance  Affords the potential to track and monitor economic activity at multiple levels, e.g. macro market, micro level and geographical levels for infrastructural planning etc.  Enhanced ability to evaluate bank credit risk - prudential regulation  Facilitates sustainability of credit market  Fosters economic growth

19  Can access “more-available” credit at more affordable rates  Ensures a faster response to credit applicants  Enables customers to shop for the best product/deal  Incentivises consumers to manage finances to maintain good payment histories to ensure continued access to credit, if consumers are properly educated on the value of their credit profiles   Reduces the likelihood of over-extending customers  Enables customers to effectively check and challenge credit information at bureaus  New consumer agents services assist consumers in understanding their sending patterns by analysing their credit profiles

20  Distinguishes good & bad risks, allows for relevant management tactics  Enables deployment of appropriate risk management measures  Increases approval volumes and speed of response to customers  Streamlines and accelerates customer take-on processes  Affords more accurate evaluation of the risk of each customer  Supports better assessment measures  Assists in reducing default rates, makes for more profit and taxes!  Allows assessment and monitoring of portfolio quality  Assists in identifying and reducing the incidences of fraud


22 Legislation and Regulation Bureaus Data Sources NCA compliance Reports to clients/CPA members Also to the CPA office on data quality & related issues Consumer access for dispute logging NCA & S69 NRCA soon FICA POPI Consumer Protection Act Consumers Public vehicles e.g. courts CPA members Bureau clients Consumers

23  Demographic descriptors  Logging of enquiries made by parties reviewing the profile  Payment performance – monthly ageing  Performance indicators– ‘adverse’ descriptors  Public domain data – e.g. judgments  Dispute loggings if these exist  Debt counselling indicators

24 Association Members submit data in standardised format, with standardised descriptors to make for easy and consistent interpretation… Bureaus receive the data & manage it according to their own data management protocols – hence the value of the standardised CPA approach… Consumers receive the benefits of the utilisation of this data e.g. rapid decision making, risk-based pricing, tailor made products, payment plans and options…

25  Of the 71.17 million accounts, 51.92 million = 73.0%! were classified in good standing, only 1,7% up year on year (NCR- CBM)  Adverse listings have stayed under 6% of total accounts since December 2010, highest ever 5.5% - September 2013 (NCR CBM)  Consider: 1.Unemployment figures; rising cost of living; additional expenses; younger population profile – majority under 35 years of age; more people into the economy 2. Risk – credit contraction & related impacts, compounding the above, but excluding people from the formal credit market!


27 Is not the data!! Is not the data, ……but rather what lies beyond it ….. For example….  Cost of doing business  Competition within the markets  Consumer earnings  Low financial literacy  What falls beyond the current NCA domain  What data is not reported &/or available for risk management & Government insights

28 Understanding and augmenting the credit data to: innovation, 2.inform policy and legislation and 3.enhances consumer choices! What’s missing from the data environment now…… ◦ Categories of data:  Spatial data deployment - geography for better planning  Debt collection data  Debt counselling data  SMME business credit consumption and payment performance data ◦ Broader reach and contribution by more service and product providers ◦ Strong guardian- and custodianship of the credit data ◦ Cognisance of the value and role of data - it is THE insight tool!


30 Strong concern that insufficient exploration and cognisance of the value, role and relevance of credit performance data and the impacts of its removal or limitation would have, i.e. beyond micro view credit assessment and extension. Data is a strategic resource for SA to optimise No economic impact assessment has been conducted (November 2013)! Please take a closer look at what is planned!

31 Guard our gold!!  Reports from past NCR CEO, also Unisa Personal Finance Research Unit within the Bureau of Market research and Moody’s indicate that removal of data will contract credit and have dire impacts on the economy, financial ranking of the country and our consumers at large.  People will be excluded from the formal credit market and will have to use informal lenders to survive – higher risks and costs will abound.  A certain reality is that South Africans need credit to survive- the challenge is to balance consumptive and productive credit usage.  The best way for the Government to be informed in order to tailor relevant and effective legislation and policy is to have sufficient information. Those insights come from explicit, quality, current, data on credit consumption, credit application/use and payment performance.   Lets address the issues where they exist within the credit sector and beyond; and not be blinded by the allure of removing the gold in our nation’s credit data.  Conduct a formal economic impact assessment before the final decision on the NCA Amendment Bill is made esp. in regard to data retention and limitation.  PLEASE lets not lose the gold we have!

32 Questions? Thank you for listening.

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