The Curious Case of the Camouflaged Rule Engines

Banks are under tremendous pressure to create tangible digital transformation use cases that aim to have an overnight impact on its brand perception or customer experience. There is also a fair bit of smart camouflage happening to the bank’s existing systems to ensure investment protection while adopting disruptive technology and staying in the game . The Rules Engine is the current muse of many an architect giving a smart makeover to the digital landscape of the bank. In its new avatar , it powers intent classification , interprets signals from smart channels and even acts as a reasoning engine while doing its classical role of decisioning , computation and comparison.

Most ‘smart’ banks aiming to ride the digital transformation wave are incing towards their 100 day goals of AI Adoption .Deliberations while evaluating strategic vs tactical goals and trade-offs between cost and time-to-market of AI enabled systems zero in on the rule-engine as a powerhouse of pre-built logic. If only the rule engine scales up to become cognitive adaptive , a number of interfacing systems may be able to draw from its intelligence. However , here is a look at how the rule engine and the smart systems are sharing space in the evolving digital space.

Intent Identification for the Workflow Bot

While the user interacts through his channels of choice, an integrator service such as the one embedded in the Microsoft Bot Framework, accepts the message and routes it towards the bank’s bot . The Bot then registers the request , passes on the request to its Chat Interpreter or the NLP engine eg LUIS. The NLP Engine can perform a preliminary intent identification based on heuristics .The Rule Engine accepts this as an input and traverses through predefined interaction patterns and zeroes in on the most accurate intent by applying an additional set of rules routing it to the most appropriate workflow or point of entry(state) in the identified workflow.

Decisioning for the Robo Advisor

Going by a similar interaction model as mentioned above, advisory services use the rule engine’s decisioning abilities to generate responses which include the following

· Calculation based on parameters (such as eligibility checks)

· Comparison across generated inputs (recommend credit cards or local offers)

· Response Selection (to advise , insist or warn — selection of tone , selection of products , selection of actions)

Interpretation for the ChatBot

Not always but sometimes the interpretation can be powered by a rule engine as well. If helps train your bot with stories that are of the nature ‘If A says this .. then B says this ‘ it can be abstracted to create if-else rules of the nature ‘If A .. then B ..’ .The rule engine cannot take on the task of training the bot but can ease decisioning or accelerate basis the nature or complexity of statement.

The rule engine’s forward and backward chaining capabilities help it to act like an inference engine as well helping build responses based on past interactions

Message Transformation for the Smart Peripherals

The future of Fintech and IoT is interlinked. From paper to computer to phone to watch — the instruction to the bank is now probably going to be at the tap of a coffee mug. With different devices and no solid standardization rules yet , the rule engine steps in leveraging its embedded library of transformation rules to enable any to any conversion messages.

It will be interesting to see how the rule engine evolves over time . Will it be merely camouflaged to power smart systems or will it emerge as the cerebrum of the AI ecosystem that helps reuse interaction patterns , evolves and self learns to leverage across product processors , smart devices and bots. In other words , will the adaptive cognitive rule engine be a contender for future investments in the AI strategic roadmap . Lets , wait and watch !