Convergence as a trend in the re-bundling phase of financial services

There is ample evidence that 3 is a magic number. It dates back to the old times and is well captured in the Latin phrase[i] Omne Trium Perfectum  – everything that comes in 3s is perfect. I bring this up not only because I chose to provide three examples in my post today but also […]

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FinServ in the age of AI – Can the FCA keep the machines under check?

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Image Source

I landed in the UK about 14 years ago. I remember my initial months in the UK, when I struggled to get a credit card. This was because, the previous tenant in my address had unpaid loans. As a result, credit agencies had somehow linked my address to credit defaults.

It took me sometime to understand why my requests for a post paid mobile, a decent bank account and a credit card were all rejected. It took me longer to turn around my credit score and build a decent credit file.

I wrote a letter to Barclays every month, explaining the situation until one fine day they rang my desk phone at work to tell me that my credit card had been approved. It was ironical because, I was a Barclays employee at that time. I started on the lowest rungs of the credit ladder for no fault of mine. Times (should) have changed.

Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks and a whole suite of methodologies to make clever use of customer data have been on the rise. Many of these techniques have been around for several decades. However, only in recent times have they become more mainstream.

The social media boom has created data at an unforeseen scale and pace that the algorithms have been able to identify patterns and get better at prediction. Without the vast amount of data we create on a daily basis, machines lack the intelligence to serve us. However, machines rely on high quality data to produce accurate results. As they say, Garbage in Garbage out.

Several Fintechs these days are exploring ways to use AI to provide more contextual, relevant and quick services to consumers. Gone are the days when AI was considered emerging/deep tech. A strong data intelligence capability is nowadays a default feature of every company that pitches to VCs.

As AI investments in Fintech hit record highs, it’s time the regulators started thinking about the on-the-ground challenges of using AI for financial services. The UK’s FCA have partnered with Alan Turing Institute to study explainability and transparency while using AI.

Three key scenarios come up, when I think about what could go wrong in the marriage of Humans and Machines in financial services.

  • First, when a customer wants a service from a Bank (say a loan), and a complex AI algorithm comes back with a “NO”, what happens?
    • Will the bank need to explain to the customer why their loan application was not approved?
    • Will the customer services person understand the algorithm enough to explain the rationale for the decision to the customer?
    • What should banks do to train their staff to work with machines?
    • If a machine’s decision in a critical scenario needs to be challenged, what is the exception process that the staff needs to use?
    • How will such exception process be reported to the regulators to avoid malpractice from banks’ staff?
  • Second, as AI depends massively on data, what happens if the data that is used to train the machines is bad. By bad, I mean biased. Data used to train machines should not only be accurate, but also representative of real data. If a machine that is trained by bad data makes wrong decisions, who will be held accountable?
  • Third, Checks and controls need to be in place to ensure that regulators understand a complex algorithm used by banks. This understanding is absolutely essential to ensure technology doesn’t create systemic risks.

From a consumer’s perspective, the explainability of an algorithm deciding their credit worthiness is critical. For example, some banks are looking at simplifying the AI models used to make lending decisions. This would certainly help bank staff understand and help consumers appreciate decisions made by machines.

There are banks who are also looking at reverse engineering the explainability when the AI algorithm is complex.  The FCA and the Bank of England have tried this approach too. A complex model using several decision trees to identify high risk mortgages had to be explained. The solution was to create an explainability algorithm to present the decisions of the black box machine.

The pace at which startups are creating new solutions makes it harder for service providers. In recent times I have come across two firms who help banks with credit decisions. The first firm collected 1000s of data points about the consumer requesting for a loan.

One of the points was the fonts installed on the borrowers laptop. If the fonts were used in gambling websites, the credit worthiness of the borrower took a hit. As the font installed indicated gambling habits, the user demonstrated habits that could lead to poor money management.

The second firm had a chatbot that had a conversation with the borrower and using psychometric analysis came up with a score. The score would indicate the “intention to repay” of the customer. This could be a big opportunity for banks to use in emerging markets.

Despite the opportunities at hand, algorithms of both these firms are black boxes. May be it’s time regulators ruled that technology making critical financial decisions need to follow some rules of simplicity or transparency. From the business of creating complex financial products, banks could now be creating complex machines that make unexplainable decisions. Can we keep the machines under check?


Arunkumar Krishnakumar is a Venture Capital investor at Green Shores Capital focusing on Inclusion and a podcast host.

I have no positions or commercial relationships with the companies or people mentioned. I am not receiving compensation for this post.

Subscribe by email to join Fintech leaders who read our research daily to stay ahead of the curve. Check out our advisory services (how we pay for this free original research).


 

 

 

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Amazon`s `Other` revenues grow 34%

We have to fly high to see what is happening in the world. We are all trapped in the convenience trap. And as David Siegel says in his recent video

We are pawns

Flying high and using the revenue lens for public companies like Amazon, is where I want to take you today. I took a glance at the 2018 revenues of Amazon. The three main businesses lines are e-commerce, cloud computing, and ad revenues. What struck me was that growth came from ad revenues which are `lumped` into a generic category labeled `Other`.

Remember 2015 was the first year that Amazon reported cloud revenues separately, revealing specifics about its AWS business. Today, four years later, Amazon reports advertising revenues in a category that is named `Other`. According to the GeekWire for 2018, Amazon reported $10.1 billion for the “Other” category. According to Amazon`s financial statements this category “primarily includes sales of advertising services, as well as sales related to our other service offerings”. Fortune reported that in Q1 2019,

Sales in Amazon’s “other” segment, which is mostly advertising, increased 34%, to 2.72 billion. The company’s digital advertising franchise has grown into the third largest in the U.S., trailing only Alphabet’s Google and Facebook, researcher EMarketer estimates.

Let me spell this out loud: Amazon`s advertising business is getting ready to be publicly disclosed as one of the main businesses competing openly with Facebook and Google`s Alphabet. This is important because the top marketplaces are Ad driven and don’t seem to intend to switch from that business model. Actually, it isn’t easy for them to switch to another marketplace business model.

Are you aware that merchants that want to sell on the Amazon marketplace have to compete amongst themselves to reach end customers? That means, paying to advertise on Amazon in order to move algorithmically up the ranking on the Amazon marketplace. This is the game that each and every Western Bigtech uses in its closed ecosystem. You have to understand the algorithm and pay to play based on the rules of the algorithm; be it Amazon marketplace, Facebook, Alphabet.


This realization makes me think that maybe, I only say maybe, merchants borrow from the SME lending arm of Amazon, to finance their advertising campaigns on Amazon. So, Amazon wins twice. I don’t have data on this, so it is only a conjecture.

We know that the technology is there to launch an e-commerce marketplace that vendors can reach end customers (B2C or B2B) without having to pay high advertising fees and incur costs to play on the platform whether they sell or not. Who can execute on this? We just need one success story of such disintermediation. Will it be in selling books or music or baby formula or online education? Will it happen in the West or the East? Will Amazon dare to cannibalize its e-commerce business at least in one area?

What we do know, is that it won’t happen from Facebook whose business is 98.5% based on advertising and their plans for a Facecoin won’t change that business model. It won’t come from Alphabet either, who earns 15% of revenues from non-google ads but 70% from advertising of the Google family (Youtube, Gmail, etc). Both are Titanics in advertising and can`t disrupt themselves.

Efi Pylarinou is the founder of Efi Pylarinou Advisory and a Fintech/Blockchain influencer.

 I have no positions or commercial relationships with the companies or people mentioned. I am not receiving compensation for this post.

Subscribe by email to join the 25,000 other Fintech leaders who read our research daily to stay ahead of the curve. Check out our advisory services (how we pay for this free original research).

Margin lending with no Counterparty risk– the Dharma open source protocol

In November 2017[1], I spoke to Nadav Hollander in California, the founder of Dharma.io, who had just “graduated” from Y-combinator. At the time, he described his vision to create on the blockchain a tokenized marketplace for loans. In February 2018, the Dharma open source protocol went into alpha testing.

Developers could easily use the Dharma libraries to:

  • Allow would-be borrowers and lender to generate open loan requests for debt agreements of any kind
  • Allow lenders to fill loan requests, formalizing a lending agreement with a borrower
  • Allow users to manage their lending portfolio by making repayments, collecting collateral, trading their debt tokens, etc.
  • Earn fees by underwriting debt agreements generated by Dharma protocol
  • Earn fees by relaying debt agreements between borrowers and lenders

Source Hello, Dharma.js

Dharma didn’t ICO because Hollander believed that token models were very immature right now. Hollander says “I’d rather build a community of constituent users and, only if and when it makes sense, issue a protocol token.” For now, Dharma open source protocol has no native token, but each loan that is created is a token itself

Fast forward to today, February 2019, one year later and Dharma raised $7 million from big investors including Coinbase Ventures who naturally are interested in crypto lending markets, especially for traders. Dharma has already launched the Dharma Lever product (in alpha mode) that deploys smart contract’s to offer margin loans for crypto traders from high volume investors.

No counterparty risk (smart contract risk, since assets are held there).

Instantly, at very low cost.

Lower borrowing rates than centralized exchanges.

Compatible with all wallets.

Screen Shot 2019-02-11 at 09.26.37

Dharma is in the same league as Maker – be your own bank or Defi[2] – that allow us to borrow against our Hodlings. Dharma involves no DAI and accommodates several cryptocurrencies beyond ETH. They are even looking to add WBTC soon which went live on Ethereum just last week.

WBTC – Wrapped Bitcoin is an ethereum-based token that is backed one-to-one by a regular bitcoin BTC.

It is already listed on several DEXs[3] including Radar Relay, Kyber Network, and AirSwap.

Dharma is changing the crypto lending space with their Lever offering that eliminates counterparty risk and replaces it with smart contract risk.

domino

The Dharma Lever is one way to mitigate systemic crisis due to the domino effect of counterparty failures.

[1] I introduced Dharma in my Feb 2018 post Bonds & loans on the Blockchain along with Tzero and Nivaura.

[2] Defi = Decentralized Finance, see more here.

[3] Read more about DEXs in `Are Decentralized Exchanges part oft he bottom up decentralized monetary policy?`

 

Efi Pylarinou is the founder of Efi Pylarinou Advisory and a Fintech/Blockchain influencer.

Get fresh daily insights from an amazing team of Fintech thought leaders around the world. Ride the Fintech wave by reading us daily in your email.

 

Fintech India boosted as Blockchain Consortium for SME lending kicks off

Fintech India saw a boost in 2018 with over 132 investments in startups, with a large proportion of them going into Lending and Insurance. The total investment was about $2 Billion as of Nov 2018. Sequioa, Omidyar, and Kalahari capital were the top investors in the sector.

Image Source

The New Year opened with a bang as 11 Indian banks have now come together to form a Blockchain consortium to address the under served SME lending market.

The rise of India Fintech in comparison with the likes of China, is still dwarfed. However, the policy makers have provided ample support to the innovation ecosystem to thrive. Initiatives such as NPCI (National Payments Council of India), Digital India Programme have helped.

The Reserve Bank of India (RBI) has approved 11 fintech firms
who could now be payment banks that offer deposit, savings, and remittance services. Unified Payments Interface (UPI) has been the bedrock of the digital payments boom in the country.

You are probably thinking – too many TLAs (Three Letter Acronyms), but the impact of all these measures on digital payments and lending in the country has been significant.

Inspite of all this, the SME lending market in India has been particularly challenging. SMEs in the country relied on a complicated supply chain that was broken and lacked transparency. A Blockchain network would provide lenders with public credit data, that they could use for their underwriting decisions.

The Micro SME lending market is about 17.3% of the overall corporate lending market in India. And after the recent IL FS scam, the corporate lending market needed a boost to tap into the under served SME sector. The 11 banks involved in the Blockchain consortium would first reach out to supply chain vendors and get their records digitsed.

The consortium includes names like ICICI, AXIS and State Bank of India, who together make up a big proportion of the lending market. Getting them all on a single network along with digitised supply chain information, should allow them to make near real time lending decisions to Micro SMEs.


Arunkumar Krishnakumar is a Venture Capital investor at Green Shores Capital focusing on Inclusion and a podcast host.

Get fresh daily insights from an amazing team of Fintech thought leaders around the world. Ride the Fintech wave by reading us daily in your email


BBVA and Porsche – Is DIY Corporate lending on Blockchain the future?

Earlier this month, BBVA announced an acquisition term loan offering on Blockchain, where they lent $170 Million to Porsche.

An acquisition loan is a loan given to a company to purchase a specific asset or to be used for purposes that are laid out before the loan is granted.

– Investopedia

The credit line will allow Porsche to expand their retail distribution channels in Europe and Asia. This is yet another feather in the cap for the BBVA, as they set to establish themselves as a front runner in providing innovative financial services.

In executing this credit line facility, BBVA have managed two firsts – first acquisition term loan ever arranged through blockchain technology, and Porsche Holding is also the first non-Spanish borrower using this technology for the negotiation and closing of a corporate loan

For the BBVA, this is by no means their first stab at something adventurous with Blockchain. Earlier to this, they have offered a syndicate loan on Blockchain for $170 Million to Red Electrica. They also offered a line of credit with Repsol for $367 Million. But this is the first time they have extended it to a non-Spanish borrower.

The press release from the BBVA discusses the benefits of using Blockchain in their Corporate lending process. From automating negotiations and minimizing operational risks, to bringing transparency and immutability to the documentation, the technology adds efficiency to the lending process.

“Our aim is to improve clients’ experience by simplifying processes and enhancing the speed of execution”


Frank Hoefnagels, Head of BBVA CIB in Germany

But BBVA have high ambitions and believe that the technology can help convert corporate lending into a “Do it Yourself” process for their corporate and business clients.

This might yet be another PR stunt, however, if they manage to achieve it, the benefits that framework would add is immense. That can be a blueprint for banks and alternative finance firms to use as a lending operating model for SMEs.

There are firms who have managed to gather a lot of intelligence around lending to SMEs. One of my portfolio firms Funding Xchange is a champion at that. That intelligence acheived through facilitating business loans over the years combined with the process efficiencies and seamlessness that Blockchain could potentially offer would create impact at scale.

It is a year when many crypto dreams have crumbled. But dream we shall, for its the season of hope. And as the New Year dawns, there can be only one way forward – Onwards and Upwards. Happy New Year folks!!


Arunkumar Krishnakumar is a Venture Capital investor at Green Shores Capital focusing on Inclusion and a podcast host.

Get fresh daily insights from an amazing team of Fintech thought leaders around the world. Ride the Fintech wave by reading us daily in your email