A Primer on Electronic Bill Payment and Presentment 

Since its inception around 1984 ,the Electronic Bill Presentment and Payments (EBPP)  business has seen a continuous evolution in terms of business models , technology components and services offered by bank . Governments have taken key initiatives to ensure that clearing houses and regulatory bodies support the growth of homegrown EBPP Models. It has emerged as a key change-driver in the way corporates  manage the end to end billing and collection cycle and paved  way for a new wave of consumer-centric digital  payments and product innovation .

EBPP Models

Biller Direct Model – The biller presents his bill data on his website and the consumer logs in and pays .In this model , the biller assumes primary responsibility of presentment of an interactive bill on his website .He may create variations such as email based bill summary notifications with embedded hyperlinks. It becomes the onus of the biller to ensure timely bill presentment and meeting the challenges around the same in terms of formatting , translating , user experience .The biller also has to integrate with a bank gateway or alternative e-commerce channel facilitate payments .This model although most effective is an expensive proposition and sees huge scope of improvisation around cost control and shared services.

Biller Direct,EBPP
Biller Direct

Consolidator Model – A single website where the consumer can go to a single place to pay his bill across multiple billers and view statements across his accounts. The biller sends the bill summary to the consolidator and almost outsources the collection cycle to the consolidator .The consolidator conducts follow ups and maintains aggregated statements for the consumer.

Thick Consolidator Model  is where the bill payment is facilitated within the same website.

EBPP,Thick Consolidator
Thick consolidator

Thin Consolidator Model is where the consumer is redirected to the biller’s website to pay.Often billers who are active participants in the biller direct model do not want to refrain from giving the consumer the benefit of the consolidator model .This is where the think consolidator model seeks adoption.

EBPP,Thin Consolidator
Thin Consolidator

Internet Post Office ModelWhere a single hub becomes the one stop shop for all consolidation and payments. This is where the biller , consumer and the financial institution log on to present ,pay and aggregate. This is a model that is emerging and gaining fast adoption.

IPO
Internet Post Office Model

EBPP Technology Components

If we look at key technology components that facilitate the Biller driven EBPP cycle of raising debits, they can be broadly classified into the following

EBPP Portal – Billers can upload and view outstanding receivables , check statements , communicate with customers .This is coupled with the back office EBPP systems that sit within the bank.The portal could be a bank ,biller of IPO style website.

EBPP Connector – A robust 24×7 connectivity software between the bank and the corporate that ensures secure bi-directional communication and allows the corporate to send across billing data in a convenient format.

Pre-Processing Hub – Once the data is transmitted, the files are parsed , data extracted , validated and sent to downstream systems .

Mandate Management Software – Holds written or electronic authorization for billers to debit consumer accounts with appropriate restrictions of date , frequency and amounts

ACH Processor – Holds mandate authorizations and can connect with the  regional or national clearing house seamlessly and send out debit requests , refunds and cancellation instructions

Lockbox – Retail or Wholesale lockboxes that may be physical or electronic in nature.  Acts as a collection centre for the bank.

 

The Value Proposition

Significant cost reduction for billers as it allows companies a chance to move towards paperless billing. Eliminating paper automatically reduces the risk and cost of handling , dispatch and archiving. Manual processes that cause significant delay in the bill collection cycle are also automatically minimized.With scale the benefits of cost become more    evident and over time it has emerged that high volume billers such as utility and insurance companies tend to benefit most from EBPP.

Customer experience and loyalty is also a key factor why most companies invest in web technology or integrating with the most in-demand consolidation models or schemes.

With the consumer now being able to self service himself by viewing and paying bills at a time and method convenient for him , it increases the overall customer satisfaction . With embedded tools to view , compare , analyze , drill down and forecast billing , usage and saving the consumer gets value added services and a far more advanced experience from the paper based billing. Moreover, standardization of the experience and multilingual interaction ensures that success model in one country is rapidly replicated in another transcending global barriers , technology hurdles and sometimes can also be extended to meet customer support challenges through a centralized model. It also opens up a world for communication that is leveraged for targeted customer segment based marketing.The consumer is far more empowered today as he can pay, inquire ,view and compare bills on the go with EBPP now finding its way into digital payment methods and devices.

There are statistics that state 75% billers experienced savings by converting to EBPP and 17% consumers vouched for overall satisfaction scores for their billers went up.

Increasingly ,central banks have shifted focus to create fraeworks for bill presentment and payment to come under a nation centralized authority that ensures standardization , interoperability and access to billers and cosumers. After Saudi Arabia’s success  , India is the next to launch its Bharat Bill Payments System. More about it in another post.

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 wit.ai 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 !

The Year of the Banker Bots

With digital banking taking new age dimensions and moving beyond the standard automation and user experience , there is a notable shift in the rise of investments in intelligent systems across the globe . 2017 is predicted to be the real game-changer where the bots are going to greet , guide and operate the bank.

Geared up for Botsification

With 11,000 bots live on Facebook messenger alone and 23,000 developers having signed up for building bots this year , get ready to chat with your service providers.As per Gartner 38% of consumers have already used a virtual assistant for services on their mobile phone in 2016  . The bots in fintech are typically either goal-based dialog agents or chatbots. This is the year of finbots

Over the last 2 years the Bot builders have reached a certain state of momentum if not maturity and therefore today ‘botsification’ of processes is no longer sounding far-fetched or experimental. Google , Facebook , Microsoft , Apple and every company that matters has invested heavily in bots and now have bot building frameworks ready for mainstream adoption.

Is Banking Ready for ‘Botsification’?

Banking as an industry is not a leader in the adoption of chatbots but there is a conscious decisioning if not adoption process underway in most banks with a careful and definitive digital strategy.  Healthcare in the US , has seen the maximum advancement in mainstream operations , with bots playing a major role in the transcription process. Most of these conversational interactions with a bot are now HIPAA compliant with mainstream service providers stepping up on the compliance front. Banking is yet to enforce Regtech in to the ‘botsification’ arena and is toying with dialog agents and customer service chatbots as its step one into the world of adoption of AI .  However , the use cases or area of engagement in most banks is clear and evolving.

Banks that have Invested Already

Santander UK introduced the Santander Smart Bank app where customers could speak to their mobile phone and ask queries related to their accounts . Future versions promised to have voice enabled payments and reporting of lost or stolen cards. Very ambitious! However, I have my reservations on bot enabled payments because artificial intelligence as a technology relies greatly on historic data and pattern recognition. Unless there is security to match up this may be a step to premature.

In April last year , Swedbank adopted Nuance’s Nina as its intelligent virtual assistant based on NLU technology. Nina offers a conversational experience to Swedbank’s mobile customers . Nina offers its chatbot primarily as a healthcare transcription engine . We are yet to measure the nature of success Nina can offer Swedbank .

Erica , Bank of America’s chatbot due for launch in 2017 is a shift away from the conversational bots where the conversation is initiated by the consumer . Instead , Erica can drive conversations . BoA promises ‘Questions , Answers, Insights’. Insights is what is intriguing the community. Erica is going to be able to guide a user on spending habits and also track credit score. Experts such as Chris Gledhill think Erica might be the ‘coolest’ entrant if industry rumors are to be believed.

Closer to home , HDFC launched it OnChat platform through Niki.ai’s  chatbot integration last week. Niki is a great platform to start building your services but its NLU capabilities cannot yet be commented on.Measurable goals for such initiatives in terms of business and engagement are yet to be ascertained as experimental or mainstream.

The DBS Digibank’s Mykai powered by Kasisto is perhaps one of the most conversationally mature bots to have entered the arena this year. Not surprising as Kasisto is a spin-off from SRI International that powers Apple’s SIRI.  Mykai is currently available on app stores in the US.

Bank / FI BOT Tech  Vendor Phase
Santander UK SmartBank Nuance and built in assistane with Santander Universities Live
Swedbank NINA Nuance Live
Bank of America Erica Homegrown To go live in 2017
HDFC OnChat Niki.AI Live
DBS , Digibank MyKai/Kai Kasisito spun off SRI International(makers of Apple’s SIRI) Live
RBS Luvo IBM watson Was to launch in December with 10% of Scotland customers
Axis Bank Not known Active Intelligence PTE

Active.AI

Announced its award of contract to the tech vendor
ABSA Not known Not known Announced intent
Bank of Tokyo Mitsubishi Nao. This  is not a chatbot. It is a  pint sized interactive robot Aldebaran Robotic (Softbank Robotics) Early 2015

 

Mizuho Pepper. Aldebaran Robotic (Softbank Robotics)
Yes Bank Payjo To Launch in 2017
RBL Payjo To Launch in 2017


The Bots will Grow Up Over Time

It is going to be interesting to see how the bots grow up over time. The investment in these bots is also with the intent that artificial intelligence is a continuously learning technology. The more you talk to your bot, the more intelligent it gets . Well , the historic embarrassment that TAY caused Microsoft merely 16 hours post its twitter launch will ensure banks invest in technology to monitor and control bot activity . While budgeting for the investment in bots , it is equally important to invest in companies who are working on technology that empowers and secures the bot.

Generative , Hybrid or Heuristic ?

While the tech providers today are using primarily the ‘Retrieval Based Model’ , it is foreseen the Chatbots build on the ‘Generative Models’ will survive the test of time.

A Generative Model takes as input the user message and the previous message to generate responses. It urges you to talk more to it and divulge more information , before it responds.

On the other hand  , the Retrieval Based Model relies primarily on a database of responses – it assimiliate context , user message and retrieves the closes pre-defined response through syntactical algorithms .Context may include a position in the dialog tree. In case it does not use context , it will respond only basis the last message ensuring stateless retrieval.

Alternately , there is also the Pattern Based Heuristic Model which engineering responses . The chatbot traverses through multiple patterns prior to responding.

Finally , a hybrid model that uses a combination of generative for intent classification and retrieval based for entity categorization , pass them through a heuristic model (or rule engine ) for response

Usability drives Adoption

The ultimate test which is goes beyond the Turing Test is that of a user’s acceptance of the offered conversational experience. Will it be dismissed as an annoying addition to the mobile phone or a trusted personal assistant , only time and technology will tell.