Evolving Credit Reporting Industry: Trends, Challenges and - - PowerPoint PPT Presentation

Evolving Credit Reporting Industry: Trends, Challenges and Opportunities

February 13, 2020 Collen Masunda Financial Sector Specialist

Agenda

2

Credit Reporting 1.0 and 2.0

1

Innovations, Trends and Development

2

Risks, Challenges and Opportunities

3

Future of Credit Reporting: Credit Reporting 3.0?

5

Implications of the changing landscape

4

Conclusion

6

Credit Reporting History

HISTORY OF CREDIT REPORTING

4

• Small community/ region based
• Narrow subscriber base
• user group model
• Oligopolistic market
• Negative reporting
• Credit scoring and automation of

workflows

Credit Reporting 1.0

• Hub and Spoke Solutions
• Bureau as a portal
• Full file reporting
• Increase in subscribers
• Entrance of new players
• Value added services:
• automated lending solutions
• fraud alerts

Credit Reporting 2.0

Innovation, Trends and Developments

Evolving Credit Reporting Landscape

6

Proliferation

• f Big Data

New Technologies New Business Models

Transaction Platforms Mobile Devices Psychometric Test Social Media Advanced Computing Artificial Intelligence Electronic Payment System Distributed Ledger Technologies (Blockchain) Open Data Systems Biometrics & National ID Systems Alternative Lenders Alternative Risk Management & Scoring Analytic Companies

Risks, Challenges and Opportunities

Risks, Challenges and Opportunities

8

Incomplete information sharing Cyber risks Data privacy Cost Reputational risk Human capital capacity Data protection Inadequate legal and regulatory environment Disintermediation/ Exclusion Potential for discrimination Consumer protection Financial instability

Risks & Challenges

Opportunities

Unique identification Data security Digitization

• f data

Cost, Competition, Data Processing Efficiency Broad product

• ffering

Data portability and mobility Data accuracy and

• wnership

Mainstreaming

• f alternative

data

Advance scoring techniques

IMPLICATIONS OF CHANGING CREDIT REPORTING LANDSCAPE

IMPLICATIONS OF NEW TECHNOLOGIES

10 Advanced /Cloud Computing Biometrics & National Identity Systems

• Improved data subject identification
• Cross border identification of data subjects – unique global

ID?

• Cross border data sharing
• Value added products & services – shared eKYC, fraud alerts
• Increased

reliance

• n

third/ fourth party players heightening cyber risks

• Legal and regulatory risks - data residency issues
• Bureau system as a services - lower set up and running

costs (plug and play models); reduced time of implementing bureau project,

• increased competition

IMPLICATIONS OF NEW TECHNOLOGIES

11 Proliferation of Big Data

• Inclusion of thin and/or no file customers
• Data integrity issues
• Review of definition of credit data
• Expansion in credit reporting ecosystem
• Data driven decision making and product & services
• Data privacy concerns

Open Data Systems

• Increased data availability
• Removes incumbency advantages and opens up the industry

(foster competition)

• Data as a public good and not source of competitive advantage
• Potential change in data ownership model
• Improved product and service innovation

IMPLICATIONS OF NEW TECHNOLOGIES

12 Distributed Ledger Technologies (Blockchain)

• Consumer sovereignty/ permissioned data models/ data vaults
• Data portability and mobility
• Cross border information sharing
• Improved speed and security

Artificial Intelligence

• Increased appetite for more data
• easier, faster and more cost effective to mine vast quantities
• Improved model power
• Irresponsible usage of AI & ML – discrimination

APIs

• easier, faster and more effective data transfers
• access to more data sources
• heightened cyber vulnerability

IMPLICATIONS OF NEW PLAYERS

13

• Increased competition:
• shrinking margins
• product and service innovation
• Mergers/ acquisitions/ strategic partnerships
• Regulatory arbitrage – regulated vs unregulated players
• Potential for instability

Future of Credit Reporting

CREDIT REPORTING 3.0

15

Business Model

• Bureau as a service
• Access on demand model
• less and less data hosting

and ownership

Data Sources

• More

and more data providers:

• Public Registries
• Open data platforms
• credit vaults/ passport

Products & Services

• Core

& Value adds – digital platforms, eKYC, fraud, credit passport

CONCLUSION

16

• Fundamental model of credit reporting will not change.
• Investment in infrastructure and digital foundational stacks important.
• Regulatory support is critical.
• Localization/ residency rules require a rethink.
• Revision of definition of credit data/ responsible mainstreaming of

alternative data.

• Guidance on usage of new technologies and innovations – FEAT

Principles on AI.

• Human capital development (private and public sector).

THANK YOU