Overview

This white paper will demonstrate how organizations serving vastly dissimilar risk profiles and growth strategies typically have very similar challenges.

Overview

In the US and Europe, businesses have long sought financing from non-bank alternatives. More recently, propelled to a very large, multi-billion dollar industry by Alibaba in China, business-to-business (B2B) alternative lending is now a major force in global finance. A multitude of providers serve the B2B market, focused on small and medium businesses (SMBs):

• Peer-to-peer (P2P) lenders
• Factoring lenders
• Merchant cash advance providers
• Direct lenders
• Pure-play digital banks
• And many more to come, as innovative new business models emerge.

Overview

Whitepaper

Overview

Whitepaper 

Overview

Analytics revealed latent portfolio risks—and levers to fix them

Overview

How split-second analysis of conversations can boost compliance and performance

Overview

THE LENDING EVOLUTION: PREPARING
FOR SUSTAINABLE CONSUMER LENDING
GROWTH

Overview

The purpose of this paper is to explain what payments industry participants need to know about the U.S. adoption of EMV, particularly with respect to fraud prevention and security. This paper covers the anticipated adoption schedule laid out in the conversion road maps that the card networks have provided, explaining what is and is not required for merchants, issuers, acquirers, and processors.

This paper also explains the security features of EMV, addressing specifically what EMV will bring to the United States while debunking the myths and misinformation. Finally, this paper analyzes the results of EMV adoption in countries that have already completed the migration, particularly with regard to EMV’s impact on fraud dynamics, and discusses how these precedents can be expected to play out in the U.S.

Overview

Five Keys to Out-Pricing (Not Under-Pricing) Competitors

Overview

Does AI + Big Data = Business Gain? This paper covers three AI-based analytic technologies already delivering business value or on the brink:

• Neural networks—including much talked-about “deep learning” techniques.
• Self-learning analytics that adapt to new fraud schemes and cyber security threats.
• Neuro-dynamic programming for making decisions with greater long-term payoffs