Tag: software

At some point a lender may need to issue an RFI or an RFP for a credit decisioning system. In this latest installment of “working with vendors” let’s dive into some best practices for writing RFIs and RFPs that will help you more quickly and efficiently understand the capabilities of a vendor. First, have one person (or at most a very small group) review the document before it goes out to vendors. Too often these kinds of documents seem like they’re just cut and pasted together without any concern if they paint a coherent picture. If it’s worth the time to write an RFI/RFP, then it’s worth the time to get it right so that the vendor responses make sense. If your document paints an inconsistent picture, a vendor may not know what products will best serve your requirements. In turn, precious time will be wasted in discussions around what’s being proposed. Here are some things to make clear in the document: For what part of the credit life cycle does this RFI/RFP apply (prospecting, origination, account management or collections)? If the request covers more than one part of the life cycle, make clear which questions apply to which part of the life cycle. Do you need a system that processes in batch or real-time requests (or both)? For example, a credit card account management solution can process accounts in batch (for proactive line management), in real time (for reactive requests) or possibly even both. Let the vendor know what it is you’re trying to do, as there may be different systems involved in processing these requests. Do you want this system hosted at the vendor, a third party (like AWS, Azure, etc.) or installed on premises? If you have a preference, let the vendor know. If you have no preference, ask the vendor what they can support. In general, consider playing down or skip detailed pricing questions. There’s nothing wrong with asking for a price range. For credit decisioning systems, detailed pricing is difficult for the vendor since there are often high levels of unknown customization to do. A better question might be, “What things will the vendor have to know in order to accurately price the solution? What are the logical next steps to get more accurate pricing? What’s the typical range of pricing in a solution such as this and what drives that range?” Will you be acting as an aggregator? Sometimes systems are created as front ends to several lenders. For example, a client may want to create a website where a borrower can “shop” among several lenders. This is certainly doable but carries with it a whole host of legal, compliance, business and technical questions. In my opinion, I’d skip the RFI/RFP in this situation and have a robust sit down directly with the vendors. This option will likely be far more productive. Ask more open-ended questions. “How does the solution perform task X?” as opposed to, “Do you support Y?” Often, there’s more than one way to accomplish a task. Asking more open-ended questions will yield a more comprehensive answer from the vendor rather than a simple yes or no response. It also gives you the opportunity to learn about the latest decisioning techniques. Be careful that you have not copied old RFP questions that are no longer relevant. I’ve had clients ask if we support Bernoulli Boxes (a mid-80s kind of floppy disk), or whether we support OS/2, etc. I’ve even had questions about supporting a particular printer. These kinds of questions are centered on the support of the operating system and not a particular vendor’s credit decisioning software. Instead of asking yes/no technology questions, ask for a typical sample architecture. Ask what kinds of APIs are supported (REST, SOAP/XML, etc.). Ask about the solution’s capabilities to call third-party systems (both internal and external). Ask fewer, but more in-depth questions. If the solution needs screens, be clear which screens you’re talking about. Do you need screens to make rule adjustments or configuration changes? Do you need screens for manual review or some sort of case management? Do you need consumer-facing screens where borrowers can type in their application data? If you need screens, be clear on the task the screens should perform. If you have particular concerns, ask them in an open-ended way. For example, “The solution will have to exchange file-based data with a mainframe. How can your solution best satisfy this requirement?” In general, state your requirement not the technology to use. A preamble or brief executive summary is useful to get the big picture across before the vendor delves into any questions. A paragraph or two can go a long way to help the vendor better assess your requirements and provide more meaningful answers to you. This works well because it’s easier to give the big picture in a few paragraphs as opposed to sprinkled around in multiple questions. To summarize, be clear on your requirements and provide a more open-ended format for the vendor to respond. This will save both you and the vendor a lot of time. In section three, I’ll cover evaluating vendors.

Perhaps your loan origination system (LOS) doesn’t have the flexibility that you require. Perhaps the rules editor can’t segment variables in the manner that you need. Perhaps your account management system can’t leverage the right data to make decisions. Or perhaps your existing system is getting sunset. These are just some of the many reasons a company may want to investigate the marketplace for new credit decisioning software. But RFIs and RFPs aren’t the only way to find new decisioning software. After working in credit services decisioning for over 20 years — and seeing hundreds of RFPs and presenting thousands of solutions and proposed architectures — I’ve formed a few opinions about how I would go about things if I were in the customer’s seat and have broken that into a three-part series. Part 1 will cover everything up to issuing an RFI or RFP. Part 2 will discuss writing an RFP or RFI. Part 3 will cover evaluating vendors. Let’s go. If you’re looking to buy new decisioning software, your first inclination might be to issue an RFI or an RFP. However, that may not be the best idea. Here’s an issue that I frequently see. Vendors are constantly evolving their products. How a product did feature X two years ago might be completely different now. The terminology that the industry uses might have changed, and new capabilities (like machine learning) might have come about and changed whole sets of functionalities. The first decision point is to ask yourself a question, “Do I know exactly what I want or am I trying to generally learn what is out there?” An RFI or RFP isn’t always the greatest way to exchange information about a product. From a vendor’s standpoint, a feature-rich, complex system has to be reduced down to a few text answers or (worst yet) a series of yes or no answers. It all boils down to nuance. On many occasions, I’ve faced a dilemma when answering an RFP question, “This question is unclear; if the customer means X, the answer is yes; if they mean Y, the answer is no.” If I were in a room with the customer, I could ask them the question, they could provide clarification and I could then provide the accurate answer. There would be more opportunity to have a back and forth, “Oh when you said X, this is what you meant ….” All of that back and forth is lost with an RFI or RFP, or at least delayed until the (hopefully selected) vendor gets a chance to present in front of a live audience. Also, consider that vendors are eager to educate you about their product. They know exactly how the product works and they’re happy to answer your questions. It’s perfectly reasonable to go to a vendor with prewritten questions and thoughts and to pose those questions during a call or demonstration with the vendor. Nothing would prevent a customer from using the same questions for each vendor and evaluating them based on their answers. All of this can be done without issuing an RFI or RFP. In conclusion, I’d offer the following points to think about before issuing an RFI or RFP: A customer can provide questions that they want answered during a demonstration of a credit decisioning product. These same questions can be used to provide an initial assessment of several vendors. A customer’s understanding of a vendor’s capabilities is likely 10x faster and deeper with an interactive session versus reading the answers in a questionnaire. Nuanced and follow-up questions can be asked to gather a complete understanding. Alternative solutions can be explored. This exercise doesn’t have to replace an RFP but instead can better inform the customer about the questions they need answered in order to issue an RFP. Don’t be afraid to talk to a vendor, even if you’re not sure what you want in a new product. In fact, talk to several vendors. More than likely, you’ll learn a lot more via a discussion than you will via an RFI questionnaire. What’s good about an RFI or RFP is coming in with prepared questions. That way, you can judge each vendor using the same criteria but, if possible, get the answers to those questions via an interactive session with the vendors. Next: How to write an effective RFP or RFI.

It seems like artificial intelligence (AI) has been scaring the general public for years – think Terminator and SkyNet. It’s been a topic that’s all the more confounding and downright worrisome to financial institutions. But for the 30% of financial institutions that have successfully deployed AI into their operations, according to Deloitte, the results have been anything but intimidating. Not only are they seeing improved performance but also a more enhanced, positive customer experience and ultimately strong financial returns. For the 70% of financial institutions who haven’t started, are just beginning their journey or are in the middle of implementing AI into their operations, the task can be daunting. AI, machine learning, deep learning, neural networks—what do they all mean? How do they apply to you and how can they be useful to your business? It’s important to demystify the technology and explain how it can present opportunities to the financial industry as a whole. While AI seems to have only crept into mainstream culture and business vernacular in the last decade, it was first coined by John McCarthy in 1956. A researcher at Dartmouth, McCarthy thought that any aspect of learning or intelligence could be taught to a machine. Broadly, AI can be defined as a machine’s ability to perform cognitive functions we associate with humans, i.e. interacting with an environment, perceiving, learning and solving problems. Machine learning vs. AI Machine learning is not the same thing as AI. Machine learning is the application of systems or algorithms to AI to complete various tasks or solve problems. Machine learning algorithms can process data inputs and new experiences to detect patterns and learn how to make the best predictions and recommendations based on that learning, without explicit programming or directives. Moreover, the algorithms can take that learning and adapt and evolve responses and recommendations based on new inputs to improve performance over time. These algorithms provide organizations with a more efficient path to leveraging advanced analytics. Descriptive, predictive, and prescriptive analytics vary in complexity, sophistication, and their resulting capability. In simplistic terms, descriptive algorithms describe what happened, predictive algorithms anticipate what will happen, and prescriptive algorithms can provide recommendations on what to do based on set goals. The last two are the focus of machine learning initiatives used today. Machine learning components - supervised, unsupervised and reinforcement learning Machine learning can be broken down further into three main categories, in order of complexity: supervised, unsupervised and reinforcement learning. As the name might suggest, supervised learning involves human interaction, where data is loaded and defined and the relationship to inputs and outputs is defined. The algorithm is trained to find the relationship of the input data to the output variable. Once it delivers accurately, training is complete, and the algorithm is then applied to new data. In financial services, supervised learning algorithms have a litany of uses, from predicting likelihood of loan repayment to detecting customer churn. With unsupervised learning, there is no human engagement or defined output variable. The algorithm takes the input data and structures it by grouping it based on similar characteristics or behaviors, without a defined output variable. Unsupervised learning models (like K-means and hierarchical clustering) can be used to better segment or group customers by common characteristics, i.e. age, annual income or card loyalty program. Reinforcement learning allows the algorithm more autonomy in the environment. The algorithm learns to perform a task, i.e. optimizing a credit portfolio strategy, by trying to maximize available rewards. It makes decisions and receives a reward if those actions bring the machine closer to achieving the total available rewards, i.e. the highest acquisition rate in a customer category. Over time, the algorithm optimizes itself by correcting actions for the best outcomes. Even more sophisticated, deep learning is a category of machine learning that involves much more complex architecture where software-based calculators (called neurons) are layered together in a network, called a neural network. This framework allows for much broader, complex data ingestion where each layer of the neural network can learn progressively more complex elements of the data. Object classification is a classic example, where the machine ‘learns’ what a duck looks like and then is able to automatically identify and group images of ducks. As you might imagine, deep learning models have proved to be much more efficient and accurate at facial and voice recognition than traditional machine learning methods. Whether your financial institution is already seeing the returns for its AI transformation or is one of the 61% of companies investing in this data initiative in 2019, having a clear picture of what is available and how it can impact your business is imperative. How do you see AI and machine learning impacting your customer acquisition, underwriting and overall customer experience?

Is the speed of fraud threatening your business? Like many other fraud and compliance teams, your teams may be struggling to keep up with new business dynamics. The following trends are changing the way consumers do business with you: 35 percent year-over-year growth in mobile commerce More than $27 billion forecasted value of mobile payment transactions in 2016 45 percent of smartphone owners using a mobile device to make a purchase every month More than 1 billion mobile phone owners will use their devices for banking purposes by the end of 2015 In an attempt to stay ahead of fraud, systems have become more complex, more expensive and even more difficult to manage, leading to more friction for your customers. How extensive is this impact? 30 percent of online customers are interrupted to catch one fraudulent attempt One in 10 new applicants may be an imposter using breached data $40 billion of legitimate customer sales are declined annually because of tight rules, processes, etc. This rapid growth only reinforces the need for aggressive fraud prevention strategies and adoption of new technologies to prepare for the latest emerging cybersecurity threats. Businesses must continue their efforts to protect all parties’ interests. Fraudsters have what they need to be flexible and quick. So why shouldn’t businesses? Introducing CrossCore™, the first smart plug-and-play platform for fraud and identity services. CrossCore uses a single access point to integrate technology from different providers to address different dangers. When all your fraud and identity solutions work together through a single application program interface, you reduce friction and false positives — meaning more growth for your business. View our recent infographic on global fraud trends