The best models should lead to the best profits, right? But many banks seem to be missing a step.
Over the last decade, few subjects have garnered as much buzz as Machine Learning (ML). This subset of Artificial Intelligence (AI) conjures images of brilliant quantitative analysts leveraging bleeding-edge methodologies to supercharge a bank’s profits. Banks hire the best minds they can find, empower them with the latest technology, and then expect money to flow in. Unfortunately, the results are often underwhelming, and a recent review by MIT’s Sloan School of Business found that “the gap is widening between organizations successfully gaining value from data science and those struggling to do so.”
Deploying AI that delivers value across the organization requires core technology that is scalable, resilient, and adaptable. Successfully implementing this foundational layer enables banks to accelerate technological innovations, improve the quality and reliability of operations, reduce operating costs, and strengthen customer engagement.
Choosing Models that Drive Profit
When adopting predictive analytics, it’s easy to become mired in the details and specific techniques.
- “Where can I get the best data?”
- “What variables should I construct?”
- “Should we use deep learning or XGBoost?”
These are all interesting questions, but we are missing the most important one: “How will improving our prediction improve our business profitability and overall value?”
The world is full of outcomes to predict, and powerful tools with which to predict them. But before we invest time and resources into modeling these outcomes, it’s critical to assess how a better prediction will lead to a stronger business. For example, we might spend weeks developing a model to predict the impact of geography on customer preferences, only to come to the unremarkable conclusion that pickup trucks are more popular in rural areas.
Pricing is one area with a direct and immediate connection between predictions and profits. A better understanding of our customers’ price elasticity allows the bank to both increase revenue on booked customers as well as drive volume by identifying opportunities with prospects who might have otherwise gone with a competitor.
Creating effective pricing models depend on a couple of important pre-requisites:
- A sophisticated end-to-end pricing analytics framework
- Precise, granular pricing structures with simulation and optimization capabilities
- Agile, real-time pricing deployment technology
Powerful models can identify optimal pricing strategies across a wide range of business goals, and integrated deployment systems can get those prices into the market immediately. Banks can develop a more profitable price-point in the morning, that creates lift by the afternoon.
As experts in the pricing and analytics field, Earnix sees leaders using price optimization to reach their portfolio goals and optimize their KPIs (i.e., volumes, margins, stock, production, etc)
Fintechs have a high level of sophistication in their pricing strategies and analytics, enabling rapid response to changes in the market or regulatory landscape. Utilizing pricing sophistication, transparency, traceability, and ensuring auditability allows fintechs to offer pricing that delivers on key business KPIs in a compliant manner. Traditional banks often struggle to keep up with regulatory changes like the EBA’s 2020 Pricing, financial conduct and/or consumer protection authorities’ guidelines. Fintechs have the infrastructure in place to immediately comply, and in many instances, to create automations that support continued compliance.
AI Leadership Revolutionizes Banking
If we examine AI leaders such as Amazon and Tesla, we see how they incorporate AI and ML expertise into the heart of their business strategies and processes, and how it is allowing them to re-think banking. These leaders are able to offer, in real-time, the right banking product to the right client at the right price—seemingly reading the customers’ minds. And these offers are delivered seamlessly to digital channels where they can be immediately seen and acted upon. This agility enables them to capture precious basis points and market shares, edging out the competition, while improving both profitability and customer satisfaction.
How are they able to do this? By putting their machine learning models to work in their production processes swiftly and seamlessly, instead of letting them gather dust in the laboratory for too long and becoming outdated.
This is no simple task. A machine learning model that may only take up a page of Python code might require weeks to properly code into a production system. Additionally, the opacity of these models often raises eyebrows with regulators, both internal and external.
In order to get the most out of these analytical insights, the Amazon’s of the world rely on end-to-end, customer-centric tech platforms, leveraging analytics to support real-time decision making. Through personalized bundles, alternative next best offers and AI-driven decisioning rules, their advanced analytics platforms help the business make quick and customer-centric decisions at any point in time.
For traditional financial institutions, AI and ML represent mission-critical initiatives to remain competitive throughout their customer’s digital journey and experience. AI is essential to supporting three important business needs:
- Automating business processes
- Gaining insight through data analysis
- Engaging with customers and employees
And when evaluating a machine learning project, there are a host of critical questions which must be addressed, and they go far beyond the algorithm with the best predictive power:
- What are the models designed to predict?
- How do these predictions drive revenue?
- How do we translate these complicated algorithms from the laboratory into our production systems?
- Where do these fit into the workflow of the existing business process?
In banks around the world, there are years of brilliant ideas and technical wizardry gathering dust on laboratory shelves because these questions were not addressed. Knowledge can be power, but only when it’s aligned with systems and business strategies.
Operationalize AI to Harness the Power of Predictive Analytics
On the technical side, implementing today’s complex machine learning algorithms into production systems is somewhere between “difficult” and “impossible.” Statistical regressions tend to take clear closed-form solutions like “(Loans Sold) = .27*(Loans Approved),” and can be easily coded into the language of another system. On the other hand, the formulas for machine learning algorithms like neural networks and random forests comprise pages of calculations. What begins as a few lines of code in the data science laboratory can balloon into a prohibitively complex task for the teams managing credit decisioning or loan origination systems. When you factor in the extra work of documentation, compliance and regular updates, many banks decide that the additional predictive power simply isn’t worth the trouble.
In order to compete in today’s analytical arms race, it’s critical to have systems that can operationalize artificial intelligence quickly, and with the rigor demanded by internal and external compliance guidelines.
Earnix provides precisely this kind of cutting-edge technology as an off-the-shelf, fully scalable real-time platform, with 24/7 support and maintenance provided in a SaaS framework. Its pricing and decisioning capabilities include comprehensive simulation and optimization capabilities, operationalizing the entire end-to-end pricing and analytics process. This includes not only determining the right price, but also enabling automated personalization and Alternative Deal Structure recommendations.
Leaders in predictive analytics clearly see prescriptive analytics as the next frontier, and Earnix allows you to hit the ground running in this new world. While predictive analytics creates an estimate of what will happen next, tools for prescriptive analytics instruct you on the best reaction, taking into account a full spectrum of bank, customer and economic behaviors.
The banking industry is rapidly building sophistication with fintechs capturing more market share every day. Incorporating machine learning is mission-critical for the survival of all lenders, and there is no time for delayed implementations or scatter-shot attempts. Banks must be precise about their goals, agile in their technical deployments and smart about how they tie it all together. Success depends on two key ingredients: banks must integrate modeling into specific processes that drive profitability, and they must also have the tools to rapidly deploy this capability into production systems.
A powerful model by itself is like gasoline in a barrel – plenty of potential energy, but only useful when it is inside of an engine. In terms of driving the business forward, a pricing engine is a natural destination for powerful analytics. And no matter how much horsepower that engine produces, it must be delivered to the wheels in order to move the vehicle forward. Likewise, smart pricing doesn’t produce results just by sitting in a spreadsheet – it only becomes useful when it is delivered to sales channels.
Informed and operationalized AI delivers banking profitability
Earnix offers a best-in-class analytical engine for calculating prices, that increase profitability without sacrificing volume. The Earnix platform has an Open API-enabled deployment mechanism to deliver those prices across all channels in real-time. Data scientists have a purpose-built framework for model deployment, empowering those models to immediately produce profitability. And pricing strategists have the power to deploy those prices directly into the market, without worrying about IT intermediaries or the complexity of downstream systems.
Few targets offer a better return on investment than pricing, but only banks with the right tools and technology to both calculate the right price and deliver that price anytime across all their sales channels, will be able to gain a strategic advantage and rewards over their legacy peers. Earnix combines a transparent, powerful end-to-end analytical framework with real-time deployment functionality, eliminating costly delays between the laboratory and the marketplace and allowing faster time to market with efficient governance and productivity tracking.