For Industries: 
Banking, Capital Markets

Financial institutions have an enormous challenge in front of them. Regulatory oversight is at an all-time high, the digital revolution has transformed how customers interact with their banks and the traditional brick-and-mortar approach to banking has been challenged by online-only direct banks. Deposit pricing is an area that is significantly impacted by all these changes and has received minimal attention from senior management in the last few years. However, as the rate environment normalizes, deposits are expected to get very competitive. A bank's pricing strategy and capabilities will be central to retaining necessary deposits. To be successful, banks need to anticipate how external factors — such as rate moves and regulatory changes — will affect the business, and revitalize focus in this critical area in order to edge out their competition.


Fraud/security protection has always been important to consumers. But until recently, it hasn't been a customer experience maker or breaker. Today it is. Consumers increasingly put fraud/security protection in their top two criteria for choosing financial services providers, sometimes even above pricing.

This white paper discusses the emergence of fraud management as a key customer experience differentiator. We explore five analytics-based best practices:

  1. Know your customers—what it means today
  2. Align with enterprise goals for customer experience
  3. Act on what you know about your customers
  4. Mobilize every analytic advantage
  5. Give customers more control and choice

Since our foundation in June 2011, we very quickly reached a point where we required an automated IT-based system for our AML strategy. Besides of being fully complianct with Latvian and EU-based laws and regulations, we wanted to relieve our employees from annual work and secure the highest security standards.


Our main objective is the sustainable fight against financial and white collar crime. We help banks, insurance companies, and industrial corporations fulfill their compliance obligations, avoid reputational damages, and live up to their own ethical standards.

In close collaboration with field experts, we have continually invested in expanding our solution portfolio to fully cover the manifold aspects of compliance. As market leader, we provide daily evidence of the capability of our software in the most stringently regulated industries and countries. The future will see us continuing our timely response to the dynamic development of statutory requirements by fully implementing these in our solutions.


The development of anti-financial crime measures has typically been relatively ad hoc and evolutionary, with banks responding tactically to immediate and/or developing fraud threats and regulatory demands. This has generally resulted in a fragmented approach, with fraud in particular often dealt with by payment type (e.g., credict car, debit card, Automate Clearing House [ACH], wire, or check), fraud type (e.g., internal, application, identity, card present on CNP), and/or channel type (e.g., contact center, online, mobile, point of sale [POS], or automated teller machine [ATM]). 

For Industries: 
Banking, Banking, Retail

With 2014 — "The Year of the Data Breach" — still fresh in every executive's mind, 2015 has gotten off to an equally chilling start; malware launched by the "Carbanak Gang" is alleged to have netted cyber criminals up to $1 billion, stolen from banks worldwide. FICO's executive team weighs in with its first Cyber Security Analytics Hot Topics Q&A.


Many decision makers are struggling to select a single AR automation solution from the numerous choices before them, however. PayStream analysts attribute reluctance to adopt this technology to the overwhelming number of options, along with a lack of knowledge about solution choices and benefits. This PayStream Advisors Technology Insight report focuses on AR automation solutions to automate the collection management process.

The report will help readers:

  • Explore different types of collection automation strategies and solutions 
  • Understand the case for implementing automated technologies to improve accounts receivable 
  • Take the next step towards the adoption of AR automation 

Even if you consider your C&R operations to be different from the crowd, this white paper will demonstrate how organizations serving vastly dissimilar risk profiles and growth strategies typically have very similar challenges.  The pressure on better managing the collections and recovery (C&R) industry in emerging markets is increasing and Turkey is a shining example of this reality.

Today, proactive management of collections is required to ensure timely and adequate planning in terms of strategy, execution and management of data-driven workload demands. These areas are all heavily influenced by a variety of factors, including changes in the delinquent portfolio profile, market growth, product terms and conditions, regulatory changes and other factors that could impact collections and recovery performance both directly and indirectly.

For Industries: 
Banking, Banking, Banking, Banking, Banking, Banking

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.

For Industries: 
Banking, Banking, Banking, Banking, Banking, Banking

“Garbage in garbage out” may sound cliché but that doesn’t mean it is not true, never more so when applied to building predictive models. Data analysts know that there are many ways models can go off track, yielding inaccurate or non-actionable results. Particularly when using observational data (rather than designed data), models can be infected by sample selection bias. If sample bias is ignored and not corrected, the models can lead to erroneous—and often expensive—decisions in a wide range of fields:

•Credit providers that extend credit to the wrong people see default rates rise and margins collapse.

•Political polling organizations that rely on historic or overly optimistic voting patterns suffer a hit to their reputation when actual election returns diverge from their prediction.

•Marketers may ignore promising segments of prospective customers—and miss out on the incremental revenues they would deliver—by being too wedded to historic impressions of who buys or finds their products useful

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