Digital Lending – The Strategy Game

A study by Goldman Sachs revealed that “Financial institutions stand to earn $30 billion in the next eight years (driven by a $600 billion loan market) by utilizing digital lending technology “.  A Credit Suisse report further stands to forecast the growth of this ‘600 billion worth loan market’ to grow up to $3 trillion in the next 10 years in a report published as of July 2016.

While the numbers may distract, the essence lies in the phrase ‘utilizing digital lending technology’. Is digital really the secret sauce to the pie?

And what is digital lending technology, anyway? Are we talking products, platforms or solutions a bank can go with when it comes to digital l ending?  How does it really differentiate?

To quickly box the definition of Digital Lending, it is characterized by a focus on making cheaper loans available through completely digital means but it does not end there , digital lending also focuses on automating the entire processing of this low cost asset. Typically, characterized by the following :

  • Newer Segments – Targets a completely new set of borrowers (typically small loans for the traditionally undeserved)
  • New Data Sources – Identification of new markets through new data points social media clues , behavioral analytics , data analytics
  • Intelligent Algorithms – Higher reliance on data technology and complex algorithms on aggregated data
  • Automation Technology Solid reliance on automation technology in the automated Underwriting Process with a no rip/replace method
  • Continuous Digital Dialogue The lender , borrower and guarantor/aggregator are in continuous sync through real-time notifications and event based alerts making it seamless

While banks offer traditional loan products, the market is strife with new and aggressive fintech players who are upping the game by introducing new business models.When banks explore ‘digital’ to boost the lending business, what is it that really that they seek ?

A New Sport , To Stay in the Game or Play the Champion’s Game ?

While the use of technology can help the bank modernize, the success and proof of value can be realized only by the forces that drive.  It is important to ascertain the end goals of digitization – is it an experimental and exploratory decision for the business or a definitive conscious one aligning to the larger business goals which could be anything from creating a new space in the market, achieving scale or building better customer stickiness.

Choosing the Right Fit or Fitting In

Alternative lending models such as P2P lending and B2B marketplace lending make cheap loans available to an audience that is either cash strapped or traditionally deemed credit-invisible. The fintechs may have uncovered a new segment and created a new ecosystem but banks are still key for the entire lending cycle to thrive and grow.

The P2P lending is the most popular alternative lending model that is attributed to have democratized lending. Individual borrowers take unsecured loans from other individuals who have surplus cash and are willing to lend for return on value.  Similar to P2P , B2B lending enables and empowers SMEs to receive necessary funds from a set of online direct individual and institutional investor. However, the bank still plays a key role in the entire chain.

Another model is Crowd Funding which is typically used to raised funds for a common interest and against reward or equity. Individuals and businesses have used this to raise early-stage investment, presell products, obtain market validation as well as to crowdsource ideas, engage customers, secure partnerships and build loyal communities.

On the other hand , equity based crowdfunding enables entrepreneurs/startups to raise early-stage capital in an online marketplace directly from individual investors, angel investors and VCs in barter for equity in the company. The bank relegates into the oblivion in such a model.

Collaborate and Compete

It is important that the battles are chosen wisely the in digital lending . With emerging models , competition from disruptors the bank needs to strategize on its feet. Here are some options to choose from basis the competency of the bank in terms of current integration capability , investment at hand and time to market.

digital lending strategy for banks
Banks can opt for any of these strategic options to achieve scale in the Digital Lending

Digital Build or Digital Buy? Unbundle and Latch On!

Typically, technology decision makers often take calls in terms of whether the solution should be completely developed in-house or ready off-the-shelf solutions be used. However, in the age of digitization, this no longer remains a build-buy-partner decision restricted to tech anymore. It is increasingly becoming a business call.

Today, there are ecosystems in place which bring the borrower, lender and guarantor together into a single marketplace. With no investment for lead generation or customer acquisition, or even creating a differential customer experience, the FI can simply focus on creating the best product offering on such platforms. Latching on to the ecosystem can be the simplest digital lending strategy to be adopted. This however, is not the prescribed leader’s game. It can only be one of the levers.

The most effective approach for a FI that looks at latching on to multiple lending ecosystems as an option and is open to offer products through diverse digital channel partners and aggregators is that of the ‘Unbundling’. Looking at each business process in isolation to create the most effective stack of APIs available to the marketplace is an approach that most banks are aggressively adopting.

Product , Platform or Technology

Well , digital lending is a combination of all of the above . Attempting to jot down a few of the key technology investments that are required in the cycle

The Adoption of digital technologies in the various stages can be visualized as below (Degree of Adoption Represented as Green – High , Blue – Moderate , Grey – Low)

digital technology adoption in lending
Adoption of Digital Technolgies in the Lending Landscape

End Note : Digital Lending is going to be the future as it operates with an intelligent understanding of customer’s need for funds and reaches out to ensure pro-active lending thus making loans quick and accessible and offers a bouquet of cheap and practical products. The right strategy at the right time is the key differentiator – biz models and technologies are only levers.

Latin America and Its Fintech Fervour

This is a multi-part series written for a leading global e-publications called LetsTalkPayments . To view the detailed post click on the links below

The Phoenix Rising

The first part of the LATAM Fintech Series explores the region’s challenges ,inspite of which it is becoming a leading destination for expansion and investment for the world with Fintech leading the space and playing a pivotal role in economy building. 

Argentina, Brazil, Chile, Mexico and Venezuela make up the Big 5 Economies of the region comprising over 25 countries across Central America, South America and Caribbean. While Colombia is a fast-growing FinTech market followed by Peru, Panama is increasingly finding itself become a test market for North American startups. Not to mention, Peru is Latin America’s fastest-growing economy. The Big 5 still remain the leaders and any socio-politico environmental change in them tips the trade economies for the entire region. Read on.

Age of the Distruptors

The second part of the LATAM Fintech Series explores the homegrown disruptors that are making LATAM the next Fintech destination and changing the local economy by inviting investment and changing how locals do business ,

LATAM is being eyed as a destination for expansion and investment by the rest of the world. The homegrown startups have some of the most ingenuous offerings designed to keep in mind local challenges in the banking space and drawing much applause and consumer base. Here is a quick look at the top 10 disruptors in the region . Read on

Keep tuned in for upcoming posts on this series and share your feedback on what else you would love covered.

Who pays off the Technical Debt for Fintech Startups ?

‘Technical Debt’ – Making sense of the Metaphor 

In 1992 Ward Cunningham – the man who created the first Wiki coined a metaphor called ‘Technical Debt’ . The term later went on to be called ‘Code Debt’ and sometimes ‘Design Debt’ . Extremely revered in software engineering circles , Cunningham was seen as a pioneer for design patterns and extreme programming. When he coined the word , he was writing financial software and tried to explain the misfeasance through the metaphor of debt to his boss . He went on to explain that attempting to write program without full knowledge to achieve short term results or perception is like taking a loan. Just as you pay interest when you take a loan , you have to regularly spend extra efforts for the correction of the temporary shortcut taken and likened this effort to interest. Malfeasance is when you do not repay interest just as you skip this refactoring of known fallacies in the system. You enjoy the short term benefits of what the loaned principal brings in without a long term plan to repay the interest.

Soon enough the term caught up with many developer communities and became a key programming concept used to define the extra development work that arises due to a temporary short term solution or quick fix applied in comparison to implementing the best overall design approach or architecture.

Inducing a Debt is most often Circumstantial than Intentional

The continuous race of Early-to-Market -Startups are often forced to induce technical debt due to business pressures . In businesses where problem solving and innovation sometimes supersede the technical relevance or skeletal architecture , the perils of scale and the magnitude of debt remain unmeasured but real.

Disruptive technology and the startups that ride on it live with the problem of rapidly evolving technology and standards around it – be it security , coding practices , communication frameworks , development and hosting platforms et all . Most of what is done is on a best-of-my-knowledge basis .

Do and See Approach – is the only way to implement since most often there are no guidelines to be followed. Code refactoring is inevitable is such situations. The blockchain startups are a good example of this – Early entrants had little or no formal documentation available and security standards were theoretical and no established test suite still exists. With time , concepts are understood better , frameworks come into being and and the risk of debt reduces.

Technical leadership and code ownership is important in FinTech. Sometimes lack of knowledge , documentation or collaboration among the team can be crippling as the codebase is bound to grow. Most great companies started out with an effective program by a great developer but to achieve scale or expand this very codebase has to be modular.When VCs look to invest , it is important to assess that the architecture is scalable and technical leadership is aware of what limitations exist and exactly when to refactor to scale.

So , Who really pays off a Technical debt ?

When a new CIO takes over or a new VC firm evaluates investment , it is important that technical debt footprint is assessed because really , it is they who land up repaying this debt.

Traditionally when banking was about in-house application building , the cost of technical debt was born by the bank. As we moved towards outsourced services , the service provider or service vendor bore the cost through piecemeal code refactoring . Then came along COTS (Commercial Off the Shelf) solutions and things got tricky. The risk of technical debt induced due to the urgency of one client was passed on to all other product clients as well. The value of debt began rising for the product company exponentially as it multiplied by the number of product installations. The CIO of the product company then becomes completely responsible for this. If there are VC’s or additional investment sought at such point it simply passes on the debt to the VC.

Software Asset or Liability ?

Most argue , that passing on debt is a good thing just like it is with rolling monetary debt , but how many VCs will agree? The objective is to accelerate growth for these firms and seek favorable exits . However , the investor must understand that sometimes strengthening the foundation at the cost of expansion can mean long term results.

With new tools for scanning and assessing software assets, quality of legacy footprint can now be gauged and they also help to determine what it will cost to eliminate this debt. Quantifying the technical debt in order to understand, contain, and mitigate the debt, as well as decide how to prioritise next steps can be done through tools.

Quantifying the Debt

Accumulated technical debt can lead to decreased efficiency, increased cost, and extended delays in the maintenance of existing system and the very first step starts with assessing the size of it. There are some tools to help assess as seen below –

CAST is a software that can hel to detect and correct errors in its core systems that could carry significant structural risk and thereby allows one to arrive at the monetary impact.

Figure 1 : Source CAST

Some companies offer plugins to your existing code. One such example is the Sonar plugin which can uses a proprietary formula to give an approximate a dollar figure to assess the value of debt

Figure 2 : Source SONAR

There are also consulting companies such as Cutter Consortium who do an unbiased Debt Valuation and Assessment exercise.

Deloitte is conducting an extensive study on technical debt reversal and predicts this to be one of the top digital trends in the recent years. Back in 2012 , the firm had predicted that $3.61 is the technical debt incurred for every line of code written within a typical application whereas globally it estimates the cost of debugging software is around a whopping 312 million USD.


FinTech Startups will protect precious seed capital and take shortcuts to build software and to play the catch up game or focus on innovation . Venture Capitalist firms will inherit technical debt in the entire lifecycle . It is important however , to understand the depth of the damage as well as the cost of reversal to truly measure the strength of the company and establish valuation.

Blockchain Economy – Embracing the Algorithm

As we struggle to fathom how a decentralized uncontrolled currency may be part of tomorrow’s real world economy there is a paradigm shift in how we perceive money with the onset of blockchain and the bitcoin in banking today. What’s baffling is that the entire control and intelligence lies embedded in an algorithm created by someone who remains unknown , unquestionable and now somewhat invincible.

Relying on algorithmic decisioning of how much currency should exist  in the economy

The algorithm releases rewards for maintenance of the general distributed ledger. Every time one uncovers a new block , bitcoins come into being. The rate of block creation is adjusted every 2016 blocks to aim for a constant two week adjustment period (equivalent to 6 per hour.) For every 210,000 blocks mined the rewards get halved. In other words , the number of bitcoins generated per block is set to decrease geometrically, with a 50% reduction every 210,000 blocks, or approximately four years. If the four- year reward halving continues, the value reaches zero for rewards once 21 million .The decreasing supply algorithm was chosen because it approximates the rate at which gold is mined.  There is also the theory that the total mined Satoshis (Smallest unit of a Bitcoin or  0.00000001 BTC) will reach the maximum length of a 64 bit floating point number and hence the limit,

These miner rewards are an amazing way to ensure the chain goes on. “Why would anyone sit and solve complex problems on the chain” , asked a friend  long ago even before the bitcoin prices soared ? If I told you, all the produce on the farm is yours if you water the farm , you would weigh the value of the produce vs your time , effort and resources involved and make a profitable decision. Miners across the world today , are doing just that !

Controlled supply of currency with complete transparency and decentralization in the economy

These finite 21 million bitcoins may not be in circulation at the same time or be spendable units . There may exist a case where we lose bitcoins on the blockchain due to a loss of private key getting corrupted on a device or one losing the private key address altogether. So just like you risk losing physical currency from a wallet today , losing a node could very much mean that we have lost money and it went out of circulation for good.

In the real world someone may find the lost wallet and the money may still be in circulation and in no way can an individual be powerful enough to take money out of the economy. He can of course hoard and keep undeclared amounts of it in his Swiss account. Where money lies, is always transparent on the blockchain .

Also, some bitcoins in circulation can be used to hold programmable assets that are not of the nature of a currency of exchange i.e., it could hold a unit of energy not money or any other. Being able to differentiate within the public distributed ledger, the actual amount of embedded money in these bitcoins may be difficult to monitor unless someone already has a solution that. The currency that comes into circulation is entirely agnostic to the hands that exchange value or the turn of trade – demand and supply.

Dilution of Power Equations through Logarithmic Equations

The  block chain , in a way also dilutes roles of power. Imagine , a kingdom of yore long before the printing press , where gold coins were the only medium of exchange. Some of the gold , they decide to use as ornaments or to worship the gods and this holds value but cannot be traded and some they use for trade. The king’s treasury takes a consensus how it wants to spend each unit – for warfare or welfare. In this case will gold behavior be inflationary or deflationary?

People say Satoshi conceptualized the Bitcoin as a deflationary currency to control the problem of consensual spend or beat insipid taxation. No money when being conceptualized would be designed to be deflationary. And , there is a huge debate on whether it is actually is. However , I am no authority to comment on that entire chain of thought , yet. Satoshi , probably never conceptualized the blockchain or bitcoin as money. This was probably the first use case to be adopted that leveraged the unending possibilities of crypto transmission.

The possibilities on the blockchain are endless. Its how you perceive and implement. It’s a bit like religion , you know. You try to incarnate it into its most tangible existence or you adopt it in principle as a way of doing things that we will all eventually align to.

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 !

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’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


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.