When most sports apparel brands were losing out on differentiation and everybody had pretty much nailed the sneaker business and were losing precious business to cheap counterfeits, Nike decided to do things their way . They created an alternate market of data driven innovation, fostered a digital connect with its end consumers through apps and most importantly created a community that lives and breathes the Nike brand through its wearables , apps , apparels and sports equipment leaving its competition way behind .
As it gets harder to innovate and create differentiation in banking, it is interesting to sit back and observe how Nike changed the game to retain relevance.
My initial understanding of PSD 2 led me to believe that it was just another enabler for digital payments and aggregated account information.
However , what completely missed my attention was the sweet spot of the P2P Lenders.
Traditionally banks have played the middlemen in sourcing funds from depositors and funding loan requirements of one or more borrowers. New P2P lending digital platforms match the borrower directly with the lender and use multiple data points to determine the creditworthiness of the borrower in addition to financial data provided.
Insight into Borrower Spend Patterns
While earlier the data was focused on getting account balances and historical borrowing and repayment information , PSD2 makes it available to analyze spend patterns and draw insights into the intent of the borrower as well. If money is spent on online gambling, a lender has the choice to deny loans although the borrower has a good credit history of repayment but a gradually declining average balance across his multiple accounts which was otherwise difficult to spot earlier.
Reduced Cost of Credit Scoring
PSD2 APIs will be used to source real-time account information with complete payment data of the borrower leading to better credit analysis . This entire process earlier involved multiple touchpoints for API integration and data ingestion. Yet , a lot of it was still best effort basis estimation based on predictive modelling technologies . Expensive AI and analytics capabilities provided the competitive edge to platforms and the game-changer was technology investment. With PSD2 , technology is an enabler for a fair playground with the focus being on the loan product offered and the services around it.
Additional Value Added Services such as Repayment Assistance
With the ability to now have an insight into repayments and account balances , newer players who offer repayment assistance and restructuring services even for retail banking may well spring up. That is exactly what PSD2 aims to do. Create a playground for innovation while ensuring transparency and regulatory checkpoints.
The ‘Born Digital’ Advantage
These P2P lenders or online market place lenders are born digital , architected for API banking and with PSD2 , the cost of risk management improves .
Killing the credit card
PSD2 seems to impose a threat on Visa and Mastercard . Also predicted is either a steady decline or emergence of differentiated services or pricing Point-of-sale loans may well see a rise under this albeit short term.
My PSD2 observations are fairly derived from more academic reading and I look forward to receiving comments and stories on how your institutions or clients are dealing or re-inventing with PSD2 , specifically credit institutions, lenders , fintechs and e-commerce.
Consensus is the validation mechanism that a blockchain network uses . These are the validation rules by which the network automatically validates a transaction . Consensus is required to maintain the world state of the blockchain network. While deciding which Blockchain platform to choose it is important to understand the consensus mechanism that it deploys because the underlying philosophy and risk to business depends a lot on the chosen consensus mechanism.
The consensus protocols vary basis the nature of the ledger because depending on whether the identity of nodes are identified or unknown , different algorithms may be deployed to achieve a state of consensus.
Different Types of Consensus
Proof of Work :
In order to change a record on the Blockchain, a peer needs to perform a complex computational task that requires brute computing power. This was the first consensus mechanism that Satoshi Nakamoto devised for the bitcoin network.
Some of the puzzles or proof of work functions could be Integer Square Modulo of a Large Prime, Shamir Signatures, Partial Hash inversion, Hash Sequences, Hokkaido, Cuckoo cycle, Merkle tree based and Guided puzzle protocols.
In short, the method relies on peers investing energy in doing useless work with no connection to the actual business problem or reason why they are on the network. It is akin solving a complex mathematical problem to play football . If you solve the puzzle , someone pays you to play !
Mechanism ensures that there will be only one version of truth ie. the next block creation.
Successful forking is ensured
It is energy expensive
It can take up to 60 minutes before the next block gets created.
Miners typically from countries where energy is cheap hence geographically undemocratic
Who Uses :
Ethereum Homestead Release (Ethash)
Proof of Stake :
Transactions are validated on the basis of a peer’s reputation or stake in the network. Consensus is deemed when valid peers agree. In this protocol , the miners are paid in transaction fees alone as no new coins are created. Unlike proof or work where miners race to create coins , in proof of stake the ‘validator’ invests in coins /assets of the system itself. A random group of signers may be chosen to sign off a transaction. The philosophy underlying proof of stake is distributed consensus.
Incentivizes good behaviour
Difficult to succeed on a public network
Trust and reputation of participants is the driving factor.
Used By :
Proof of Elapsed Time:
The Proof of Elapsed Time consensus protocol is what is used by Intel Sawtooth now IntelLedger (under Linux Hyperledger Foundation) . This consensus algorithm has vouched for a random leader election methodology. The leader can be any of the participating active nodes. The chosen leader finalizes the block . It is an interesting method because the system intelligently ensures that the leader is chosen without manipulation and all participating notes verify the same and also deploy random leader distribution method.
Randomness of leader selection ensures equal distribution and no chances of polarization
This is reliant on Intel’s Software Guard Extensions(SGX) .
Used by :
The Byzantine General’s Problem
Before we approach to discuss any of the byzantine models , it is important to understand the Byzantine General’s Problem.
A group of generals, each commandeering a part of the Byzantine army has surrounded an enemy city. The generals have to agree to a common battle plan to seize the city . However, the generals can communicate via messengers only. The messengers might be captured by the enemy and the message might never reach the other general. The difficulty in the agreement is that one or more generals might be traitors and interested in sabotaging the battle plan. They are likely to send false messages, distort messages or suppress messages. All loyal generals will act according to the plan. A small number of traitors can upturn the course of events in the loyal generals’ plan.
In the Byzantine general’s problem are embedded to key threats – How does one deal with failure of any one general? What if the identity of a general is forged? When applied to the blockchain network, the answer to the first question lies in the provisioning of a fault tolerance mechanism (recovery mechanism) and the answer to the latter question lies in security mechanism deployed by the Blockchain network can protect itself from Sybil attacks.
PBFT (Practical Byzantine Fault Tolerance)
The PBFT algorithm was the first practical solution to achieving consensus in the face of Byzantine failures. It uses the concept of replicated state machine and voting by replicas for state changes. It also provides several important optimizations, such as signing and encryption of messages exchanged between replicas and clients, reducing the size and number of messages exchanged, for the system to be practical in the face of Byzantine faults.Assuming there are n traitors in the group , there would need to be “3n+1” replicas , at least “2n+1” communication paths and “n+1” rounds of messages exchanged.
Used by :
Hyperledger Fabric V1.
Other variances of the PBFT are SIEVE and Cross Fault Tolerance (XFT)
Federated Byzantine Agreement:
Stellar Consensus protocol algorithm uses the concept of quorums and quorum slices. Quorum is a set of nodes sufficient to reach agreement. A quorum slice is a subset of a quorum that can convince one particular node about agreement. An individual node can appear on multiple quorum slices. Stellar introduces quorum slices to allow each individual node to choose a set of nodes within its slice thereby allowing open participation. These quorum slices and quorums are based on real life business relationships between various entities thereby leveraging trust that already exists in business models. To reach global consensus in the entire systems, quorums have to intersect. Overall consensus is reached globally from decisions made by individual nodes.
The Ripple Consensus Algorithm is another example of a federated byzantile agreement model
Used by :
Tolerant to a failure of minority of nodes
Low Latency of transactions
Inherent trust leveraged of the permissioned Blockchain
Failure of over 1/3rd of nodes will result in a lack of consensus and transactions fail to get committed
Consensus mechanisms will evolve over time but the choice of algorithm is key to the nature of the Blockchain network’s future roadmap and what services it would like to offer on it. While Hyperledger and Ripple have clearly emerged as permissioned business ledgers , Ethereum has continually toyed with the idea of different consensus algorithms as it evolves . As the world moves towards faster transactions , the success of Blockchain has much to do with the consensus method chosen and the latency involved in addition to security.
Trade Finance has been a well established and important business for Banks and Financial Institutions. Hardly any domestic or international Trade activity can take place safely and successfully without some form of trade financing, in fact as much as 80% of annual global merchandise trade is enabled through some form of trade financing. This financing can range from traditional instruments like Letters of Credit, Bank Guarantees to a more contemporary form of open account based supply chain financing.
In an ideal world, banks would have mastered the art of running their Trade business successfully over donkey’s years but due to volatility in global economy, market conditions, changing customer expectations, Fintech disruptions, risks and more stringent compliance issues; it has not been an easy ride.
Cost, Revenue, Return on Equity (ROE), Risk and Compliance are key aspects of any business irrespective of its nature and size and the same holds true for Trade Finance as well. In order to run a business successfully one needs to bring down costs, increase revenue, mitigate risk and improve compliance. These are like four sides of a square and although they look mutually exclusive but they are very much dependent on each other. It’s a classic Catch22 situation where you cannot increase your revenue unless you focus on bringing down your costs.
Key stakeholders in a bank have always been plagued with their own set of role based challenges. Here are the 4 Strategic questions that should be asked if the bank is looking to transform itself into a leader in the Trade Finance business
How can the COO minimize operational Cost, automate and improve productivity?
The main challenge faced by a COO typically is high transaction cost due to manual processes and lack of visibility. Bringing down cost is one of the biggest drivers for banks and financial institutions to look for technical advancements and automations in the Financial Supply Chain. And with Trade being very document intensive, these advancements need to happen in multiple areas of Transaction process automation, digitization using MT798 (SCORE), Bank payment obligation (BPO), e-Bill of Lading, Imaging solutions like OCR/ICR etc. The elimination of paper from trade finance transaction processing could increase throughput per transaction and process automation could potentially reduce compliance costs by 30% or more. Due to proven success, digitization is one of the top strategic focus areas for banks globally over the next 1-2 years. There are other areas of supply chain disruption such as smart contracts and blockchain which some of the global banks are now exploring but before investing in any of these technologies, a cost to income analysis needs to be done carefully.
How can a Product Head Innovate, increase Revenue and yet get a shorter Time to Market?
Single solution supporting domestic and international business of customers, lack of configurability to offer new products and cross-sell opportunities are pain areas for a Product Head.
Over the last two year, globally we have seen a drop in revenues from trade finance from USD 41 billion to USD 36 billion. Banks not only need to look at ways of driving deeper wallet share of the large corporate client’s business but also look to grow its business from MSMEs and SMEs. With these falling revenues the focus is shifting from traditional Trade Finance products like Letters of Credit, Guarantees to simpler and cheaper open account based financing products. Today more than 80% of the Trade finance is done using open account and this number on the rise. Solutions like dynamic discounting offer new revenue streams for banks without putting risks on their balance sheets. Key would be to launch these products as soon as possible. It is important therefore that the technology solution that drives the entire business can cater to domestic and international business of customers and also ensures that there is the provision to configure on-demand solutions. The flexibility to package newer rules to scale to ever changing compliance guidelines and processes to deal with newer emerging segments and dynamic pricing is imperative.
How can a CEO look to improve Return on Equity?
The CEO is always on the lookout for promoting capital light models that support originate-to-distribute strategies. With the advent of supply chain finance as banks move farther away from traditional trade, the biggest need of the hour becomes converging the cash and trade businesses to meet the needs of the end customer.
Trade Finance products are usually characterized by short average maturities and this has triggered risk-underwriting firms like insurance companies getting involved more in these transactions to mitigate risk. An ability to distribute risk across insurance companies and participating banks/investors will not only reduce risk for banks but also enable them to get more business from clients and hence more revenue. Banks need a strategy to move from “Book & Hold” to “Originate & Distribute” model by distributing the risk using one or more risk mitigating tools like funded/unfunded participation, syndication or insurance with capabilities of monitoring risks and handle NPAs by aggregating customer’s financial obligation data across multiple systems.
How can a Risk Officer mitigate Risk and ensure full Compliance to regulations with minimal cost?
A recent ICC survey lists down top factors affecting Trade business of Banks and also reflects changes in Trade revenue in the year 2016 and it’s important to notice that compliance, risk and falling revenues are seen as major challenges by Trade banks.
As regulations around cross-border transactions continue to increase, compliance costs continue to spiral upwards. Banks adapt to comply with a growing and changing set of regulations covering sanctions, trade based money laundering (TBML) and non compliant banks risk incurring heavy costs. From 2007 to 2014, fines imposed on US and European financial firms grew from $30 million to $58 billion.
The need for a 360 degree view of risk portfolio and ability to monitor exposure across clients, countries, currencies, sectors, insurance companies has emerged as one of the key asks of the Risk Officer.
Banks and Financial Institutions need to invest in technology that can encompass data analytics capabilities to provide banks with a holistic view of customer portfolio and allow them to monitor risks across various risk attributes. Automating of due diligence workflows with help of natural language processing (NLP) and Artificial Intelligence (AI) based solutions is another major leap towards bringing the cost of compliance checks down. There are companies focused on simplifying compliance and TBML processes using AI, NLP and Big Data analytics and are invaluable additions to the financial ecosystem.
Banks looking to lead the race need to be prudent and invest in the right technology and partnerships while tuning their existing processes to maximum efficiency and evaluating newer revenue generating models. For both, global banks looking to enhance their global footprint and regional banks fending off the larger global players in local markets it is important that their strategy is charted aligning closely to the bank’s larger digital investments. Digitizing Trade process is not a silver bullet to solve all the problems discussed but it is certainly a significant step towards it.
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 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)
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.
The science that simulates the engagement , decision and the discovery process of the human brain – Artificial Intelligence(AI) or Cognitive Sciences has unlocked a world of possibilities for the banking industry in particular. Cognitive systems quickly identify patterns similar to a way our brain identifies situations and responds to it.
Within AI , there are a number of different technologies and they lie at different points in the curve of adoption with virtual assistants and robo-advisors riding the crest of adoption. Semantic Technologies specifically have a world of discovery lying ahead. Although it is widely used as the underlying technology for most deep learning systems , there is a vast amount of work remaining in this area and experts believe it is only half tapped although it has been around for years.
This post is essentially focused on semantic fingerprinting and how semantic technologies are about extracting meaningful data from quantitative data , text , voice ,video and images . Unlike previous applications , data and relationships were pre-defined and read from relational databases but semantics open up a world of realtime relationship extraction and contextual inference models being built and leveraged at runtime.
Semantic Maps – Vocabulary Building Tools
Semantic Maps or Networks have been around for more than a decade where words are understood under the context they are used , very similar to how the human brain understands and processes words, phrases or a language. This technique is used as a teaching method for children learning a language and has been seen as a very powerful vocabulary building tool.
The same terminology takes on a far more complex computing context .Semantic Mapping can be used for dimensionality reduction of a set of multi-dimensional vectors to retain main data characteristics. The original properties are clustered to generate an extracted feature. This technology has typically been explored for text mining and information retrieval.
Semantic fingerprinting is a new method whose manner of processing text is modeled on that of the human neocortex . Semantic fingerprinting has the potential to be more powerful in document comparisons than are word list-based analyses.This technique is used to identify similarities , identify context and also arrive conclusions.
Since semantics are heavily dependent on context , the fingerprint for ‘Apple’ would have strong connections with the computer brand. Words, Sentences and whole texts amounting to terabytes can be compared against each other.
Source : Cortical.IO
Big Data + Semantics = Endless Possibilities
Big Data Semantics is where technology is applied to reduce the stream of unstructured data to understand , predict , categorize or sort just as much as one would with any form of data once reduced to an understandable , identifiable form.
The system ingests data in any unstructured form – emails , faxes , documents , sms , social media or data from internal systems and then runs it through a semantic engine. The semantic engine generates smart binary vectors with minimal memory footprint represented in boolean. This helps to compare aggregated or atomic representations of words .
Some ways in which it is being leveraged in banks are seen in the table below
We all know and acknowledge that larger tech giants such as IBM , Google , Apple have experimented and mastered these technologies over decades now and while IBM has focused on strong enterprise use of these technologies , Google and Apple have invested in embedding cognitive in consumer applications with specific focus on mobile tech. Also , having invested hugely across the AI technology stack , they also have a huge advantage of direct and indirectly available data corpus used for training data .This makes the competition one sided and leaves little room for startups. But what the startups are doing beautifully , is coming up with differentiated, disruptive use cases .With the rise of AI startups receiving funding across the globe , the interlocking of logically related technologies and their use cases are only to rise . We can only wait to watch for the treasures it unlocks.
Disclaimer: This post is not influenced by any of the institutions that I am/have been directly or indirectly associated with and views and opinions expressed are solely mine .
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
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.
Micropayments originally came into being around the 1990s . As the name suggests , this refers to extremely small amounts being transferred online through web protocols.
At that time online payments were expensive and the challenge was to make micropayment feasible for billing for online media publishing which meant restricting the cost to a bare minimum . While Paypal defined a micropayment as an online payment of less than 5 GBP , Visa defined it as less than 20 AUD. However , IBM Micropayments that came about around 1999 enabled payments less than one cent.
This could well have been the revolution that enabled e-commerce. However , research was stopped across IBM , Compaq but the link below traces the roots to todays payment wallets , API based payments and commerce pull push to all research around Micropayments.
While the term has stayed on , and we still refer to small payments as micropayments , the term itself refers to a league of patented technology and research done around reducing the cost of online payments.
The term Nano gap was coined by accounting firm Deloitte in around 2010 to describe the shortage of capital to fund the retirement of baby-boomerentrepreneurs seeking to sell their small, medium enterprises (SMEs).In the Deloitte report: “Micro-cap typically refers to those companies with an equity value of less than $250 million. Nano-cap is another term that is used to refer to companies with a value of less than $10 million.”
Just when we thought chequebooks and cheque processing was on its way out , we see a unique use case where the traditional banking instrument uses new age disruptive technology such as blockchain to combat cheque fraud.
Cheque fraud accounted for nearly 77% percentage payments fraud in the US alone in a survey conducted in 2015 by AFP . Almost globally the largest revenues impacted in payments fraud are via cheque fraud.
Emirates Islamic, one of the leading Islamic financial institutions in the UAE, announced that it will introduce blockchain technology into its cheques as a fraud prevention measure.
Earlier this year , Emirates NBD had announced the ‘ Cheque Chain’ initiative . There will be a Quick Response (QR) Code on each leaf which enables validation at the source through self service or at the presenting bank . The QR code will be registered on the blockchain so that once the cheque leaf has been presented and cleared under the bank’s ICCS technology , the record will be maintained on the blockchain for auhenticity .
With ‘Cheque Chain’ , Emirates Islamic becomes the first Islamic bank in UAE to undertake this initiative to enhance security in the popular payment method.
Emirates Islamic announced its plan to issue new cheque books carrying a unique QR (Quick Response) code on every leaf, along with a string of 20 random characters to make new cheques being issued compatible on the new platform.
This is an extremely smart move to combat the ever increasing costs of image based clearing and instrument handling coupled with archival and storage of the information . It will be interesting to see how cancelled cheques are handled on the platform and in the long run how the total cost of ownership for institutions span out with blockchain .
The Union Bank of India had reported a case of cyber attack on one of its nostro accounts last year on the 21st of July. At that point , the amount remained undisclosed and the breach was said to have happened from an email attachment opened by one of its employees.It was a phishing attack. An email with the handle @rbi.org.in had an attachment -a zip file with a .xer file.While one employee fell pray, some were smart to report it as suspicious. However, it was too late.The malware had entered the bank.
A sum of $171 million had been debited from its nostro account with Citibank New York .Since Swift recon happens only the next day once the nostro statement comes in , the bank’s treasury department realised only the next day.
The money by then had been moved to accounts in two banks in Cambodia—the Canadia Bank Plc and RHB IndoChina Bank Ltd, besides the Siam Commercial Bank in Thailand, Bank Sinopac in Taiwan, and a bank in Australia. These funds were routed by Citibank New York and JP Morgan Chase New York, which hold UBI’s foreign exchange accounts.
SWIFT maintains a neutral stand in the investigation primarily initiated by UBI and insists no breach at its end.However , it is high time that SWIFT looks at its loopholes -recon delays , lack of built in fraud early warning mechanism and an AI poweredneural network enabled clustering system that can track such suspicious activity.
With blockchain heralding a new age of cryptographic security and unit level transparency the bank’s must also look for alternatives to the wire transfer monopoly and it’s inherent loopholes.