ScoreFast™ for the Financial Services Industry
History shows that financial Services industry has always been an early adopter of new age technologies. And even more so with utilizing their data assets for business benefits, which is essentially what data analytics is. IT is because financial services is, at the core, business of making profits over the spread between the earnings on the assets and the expenses on liabilities over a reasonably big customer portfolio.
So the industry by definition is data intensive and success depends a lot on an organization’s ability to understand its customers, their behaviors, and to leverage those insights in day-to-day operations. This is the reason why during the early computing era in the sixties and seventies, banks and financial services institutions were the first businesses to leverage their historical datasets for important business functions like credit decisioning.
Today with the advent of IOT and big data when almost everything we do is being captured at an unprecedented rate. When cloud technologies both deliver new data sources and provide a scalable, pervasive ecosystem for analytics; the same DNA of the financial services industry is fostering an era of unprecedented innovative usage of data and machine learning technologies. On the one hand, traditional financial services firms are finding novel ways to leverage machine learning and big data to optimize standard business processes, while on the other hand new age FinTech firms like Klarna, Notion and Affirm are using all this technological power to redefine the industry itself. One example would be using social media signals and Internet footprint in the credit profiling and decision process, making it more robust and at the same time reducing turnaround times.
Data is the bedrock on which machine learning and predictive analytics stand. So in order to look at how predictive analytics and new advancements in these technologies are changing the banking and financial services industry, let’s look at all the different types of data and signals that are available to these businesses.
At a broader level, there are two types of datasets that a company has access to – business data (the data a company gathers while conducting its business- customer demographics, transactional datasets) and outside data, which in turn can be either public data (social media etc.) or private datasets available for restricted usage (e.g. credit ratings). Companies use these datasets for all sorts of purposes, but essentially to understand their customer segments, their habits, behaviors and preferences; and use these insights to inform their (the company’s) business decisions.
If we list core business functions in the financial services industry – from a business standpoint as well as from the perspective of a variety of predictive use cases for this industry, we can list the following functions: Sales and Marketing, Risk Management (fraud risk, credit risk etc.), Customer Relationship Management, and Collections and recoveries
Having established the categories of datasets and important business functions, let’s now look at predictive use cases for various business functions one at a time:
Sales and Marketing
Marketing involves investing money into campaigns in order to lure new (or old) customers into the business. In order to allocate marketing budgets optimally, it is vitally important to understand the returns on investments from various marketing campaigns historically. One important predictive use case in this arena is Promotion Response Models – which understand the interplay of promotions and resultant responses (footfalls, click-through-rates (CTRs), sales etc.) dependent on historical data. Essentially, these models help companies simulate potential sales (or other relevant metrics) basis specific promotional dollars allocation and run different scenarios according to business strategy. And then use all these simulations to come up with winning budget allocations for maximum ROI.
Another evolved area of predictive analytics application is Sales Forecasting Tools. Being able to use historical sales trends, market directions, macroeconomic data and other relevant signals and accurately foresee a future sales trend is of primary importance to any business, more so for large-scale financial services organizations. Channel Optimization is another area that is a very important predictive use case in sales and marketing functions. It entails devising an elaborate channel wise budget allocation plan for maximum ROI.
To summarize, fundamentally the focus of predictive analytics in sales and marketing functions is on improving marketing efficiency and maximizing the ROI on sales campaigns.
Being able to accurately understand underlying risks (be it fraud risk or credit risk or other risks) and use this information efficiently for business benefit is at the core of success in the financial services industry. And this is why one of the first use cases of predictive analytics in the industry was in the area of credit ratings. Today, with a lot of diverse datasets available, the industry is innovating everyday in order to improve risk management functions.
In Credit Risk Assessment functions, especially at the time of onboarding, new age FinTech firms are using all sorts of signals – from customers’ social media footprint, to social network maps (friends/ colleagues/ family), along with more traditional data sources like demographic and profile information and credit history to improve the credit decision processes – making it more efficient as well as cutting down timelines. The decision cycles are getting shorter without compromising on decision qualities, and in fact in many cases improving them. The whole cloud based distributed computing ecosystem and mobile technologies have made high end computing resources available for innovation and opened up the marketplace. New age FinTech companies (e.g. LendingClub, Affirm, Klarna etc.) are leading efforts in these areas.
A lot of predictive analytics is also used in Credit Line Management– a dynamic assessment of credit line that is to be extended to a customer based on her profile, past behavior and most recent transactional signals.
Another key area for predictive analytics applications in risk management functions is Fraud Risk Management. This comprises of all fraud risk exposures for a bank- from the time of sourcing to all transactional fraud exposures during the lifetime of a customer relationship. Financial services companies use predictive analytics to predict the propensity to fraud at customer levels as well as at transaction levels, and use this information in their risk management decision to establish acceptable risk criteria. Cloud based distributed computing architecture is allowing companies to be very nimble with their fraud risk containment decisions. Companies are using the most recent signals and trends to inform their fraud alert systems, helping them tread the fine balance between customer experience and fraud risk exposure at all times.
Customer Relationship Management
Once a customer comes on board, till the time the relationship ends, all interactions with the customer can be labeled under customer relationship management. Predictive analytics plays an instrumental role in various CRM functions. One such high impact area is Cross-sell/ Up-sell. Selling to an already existing customer makes more business sense than acquiring a new customer. If you do it right, you not only deepen your existing customer relationships but also invest your marketing dollars in the most efficient place. And on the flip side, if you don’t do it right, cross selling to uninterested customers can result in irate customers, and eroding the brand equity. Financial services companies use customer behavior data along with their demographic information and other signals to accurately predict customers’ interest for other products. Today’s machine learning tools can ingest even the most obscure signals to predict the propensity of customers to react positively to cross-sell / up-sell offers.
Another important area of predictive focus is Customer Churn. Acquiring a customer can involve big investments and churn takes away the opportunity of a business to make good on a customer relationship. Being able to successfully predict customers’ propensities to churn in a given period gives businesses enough time to run preventive campaigns and contain customer churn.
Collections is one of the core functions in the financial services business. A company’s ability to collect efficiently on its debts in today’s market depends a great deal on their ability to use the historical data efficiently. This enables them to – preempt the possible default events, predict the payment propensities etc. This helps companies to optimally allocate their collections budgets. Some established predictive use cases in the collections function are variousDelinquency Prediction Scorecards and Payment Propensity Prediction Scorecards (for recoveries portfolios).
With such a complex array of functions to perform in the spectrum of customer engagement- speed of execution, speed of anticipation and speed of delivery of offers to consumers is essential, especially for the Banking and Financial Services industry. ScoreData, with its ScoreFast™ engine makes it possible for all sizes of financial services companies to make their decisions in real-time or near real-time in the broad spectrum of applications in Sales and Marketing, Customer Churn, Risk Management, and Customer Relationship Management.
The most important need for any consumer-facing industry such as Banking and Financial Services is customer engagement. In the three years since ScoreData was founded, they have focused on building solutions for consumer facing industries. In order to further assist banks to improve the responsiveness and effectiveness of their sales and marketing campaigns, and to implement cross-sell strategies to assess customer loyalty, the analytics platform has a variety of pre-built model offerings in consumer analytics, risk analytics and other areas like churn management etc.
The ScoreFast™ platform fosters widespread analytics consumption and insights usage across organizations and has an easy to use business dashboard driven data/model development and deployment facility. Comprehensive centralized model management with version control means less duplication, more collaboration, and ease of diagnosis when model performance deteriorates.
At run time, models update themselves incorporating a wide variety of company internal, and third party and regulatory data. The platform is flexible enough to ingest new data sources or tune out old data sources during the model building process.
This ensures that the most accurate models get deployed over time. ScoreFast™ then compares and contrasts results from hundreds of in-memory-built models with these algorithms. This is a significant improvement over legacy practices, thus shrinking model-to-market times from weeks or months to days or hours. ScoreFast™ is an ideal platform for new ecosystems in the Banking, Financial Services, and Insurance
April 03, 2016