AI algorithms – takeaways from Fintech+

The Fintech+ conference with its AI thread was unique. The morning sessions included presentations from Nvidia and Google, and use cases and learnings from Zurich Insurance. Leading into the sustainability & Fintech panel that I moderated just before lunch.

Marc Stampfli, Swiss country manager at Nvidia took us on a journey of AI Fall, Winter and into Spring. He explained neural network concepts borrowed from biology and the initial difficulties of neural network computations outperforming statistical approaches. The 1st tipping point came with increased data availability through the internet, and only then we had evidence that neural networks could outperform statistical models.

After that point, we ran into the next problem which was the lack of computing power to process all this data and multi-layer neural networks. And this is where GPU – a kind of parallel computer –  was created and first used in vector mathematics. This is the technology of Nvidia’s processor.

For me, this historical thread is another example of a solution designed for theoretical mathematics that finds a real-world application that takes us to the next level of the 4th industrial revolution. I associate it to the zero-knowledge proof in cryptography, now used in some blockchain protocols, that allows to verify & validate data without having to trade-off privacy[1].

We are living in a world in which, more or less unconsciously, we increasingly “Trust in Math”. After the GPU adoption in business, we moved to new hardware that is not only faster but also smaller in size. We basically reinventing how data rooms looked.  And this the world from Nvidia’s angle. They have facilitated the growth and new value creation, all powered by #AI tools.  The use cases in Finance are immense. Fintech solutions for:

  • Operations: automating claims processing and underwriting in insurance
  • Customer service & engagement: alerting customer for fraud, chatbots, recommendations
  • Investing/Trading: automating research, trading signals, trading recommendations
  • Risk & Security: fraud detection, credit scoring, authentication, surveillance
  • Regulatory & Compliance: AML, KYC, automating compliance monitoring and auditing.

Evidently, the biggest but fundamental problem that incumbents face in adopting any of these potential use cases, is that they first need to find ways to integrate their data and then to upgrade their data rooms to be able to handle the required computing power.

Having said that, Zurich insurance, one of the large Swiss insurers, shared with us their AI projects and research which started as early as 2015. Gero Gunkel spoke about their very successful AI applications in automating the review of medical records with the aim to arrive at a valuation. A process that entails reviewing reports ranging from 10-40 pages and that may take on average 1hour. They used AI algorithms that reduced this to 5 seconds! That is nearly real time for a business process that is Not low hanging fruit.

Zurich Insurance has also been using AI to automate the time-consuming process of collecting publicly available information towards opening accounts for large corporates. This automated web search can not only offer efficiencies but also become a new service provided to the underwriters of these types of insurances.

“Don’t look for the Swiss army knife”, said Gero Gunkel as AI may seem so promising that one can think it can take care of everything.

Dr. Christian Spindler,  IoT Lead and Data Scientist at PwC Digital Services, raised the important question on how to develop Trust in AI. This is a tricky topic as it beckons for answers around the limits of technology. For now, it is recommended to develop AI algorithms that can also provide explanations for their “Answers”.

I would say that “In Math we Trust” to develop algorithms that Answer “What & Why”.

“Improving lives through AI” is Nvidia’s motto for their Corporate Social Responsibility. See their initiatives here.

[1] Zero-knowledge proof allows a someone to re-assure a validator that they have knowledge of a certain “secret” (data) without having to reveal the secret itself. Zcash is an example of such a blockchain protocol.

Efi Pylarinou is a Fintech thought-leader, consultant and investor. 

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The £50 Billion opportunity and how the global stage is set for Regtech

Regtech is a £50 Billion per year opportunity, and that is just in the UK. That is due to the hundreds of millions of pages in regulatory texts that firms have to deal with, to be compliant. It is critical that firms equip themselves with technology solutions that will help them navigate through the complex world of regulation.

Please note that while Regtech covers regulations across industries, I am taking the liberty of using this term loosely to refer to FS based Regtech use cases.

During my time at PwC, I was involved in evaluating AI products for their Legal and Regulatory offerings. We were looking into IBM Watson, and had some interesting conversations on sending Watson to school to learn Legal and Regulatory language (in English). The AI engine (deep learning, NLP) would then be able to provide guidelines to firms in plain English on what was needed for regulatory compliance.

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It has been almost five years since then and we have seen various developments across the globe. Regtech has never been more relevant. US and Europe have more than 200 Regtech firms, as these two regions are clearly seen as the pioneers of financial services regulation.

‘The FCA is the most innovative regulator in the world in terms of using new technologies and the other regulators look up to them”

– Philip Treleaven

In my opinion, Europe and in particularly the UK’s FCA are world leaders in working with innovative ways of achieving regulatory compliance. Be it payments, open banking or crypto currencies, they have taken a collaborative approach in nurturing the right firms. 37% of Regtech investments across the globe happen in the UK.

But its the happenings in Asia that I find more interesting from a Regtech stand point.

Fintech India has seen massive growth with digital payments being well backed by policies and technology infrastructure. The rise of PayTM, UPI and more recently Google Tez have all helped in bringing the total transaction volume of digital payments to $50 Billion. But with growth comes greed, and regulations have to kick in. There were tens of P2P lending firms in India until the Reserve Bank of India (RBI) launched their regulatory framework for P2P lending in Q4 2017. There are now only a handful of well capitalised P2P lending platforms.

There is a lot of work to be done around automation of transaction reporting. For example, the Microfinance market in India is still largely cash based and reporting is manual. There are startups trying to disrupt this space with cloud enabled smart phone apps, that allow for real time reporting of transactions, when an agent is on the ground collecting money from a farmer. This allows for massive gains in operational efficiency, curbs corruption, but more importantly helps transaction reporting so much easier.

I see India as a market, where Regtechs can help the RBI develop a regulatory framework across Financial Services.

China’s P2P lending market is worth about $200 Billion. Recent frauds like Ezubao, where about a million investors lost $9 Billion, indicate that the market needs to have strong regulatory controls. The scam led to a collapse of the P2P lending market in China. A regulatory framework that helps bring credible players to this space, well supported by a bunch of top Regtechs will help the status quo.

Singapore is the destination for Regtechs in Asia – without a doubt. After the US and the UK, Singapore attracts the most investments into Regtech firms. The support that Monetary Authority of Singapore (MAS) provides to budding startups is the real differentiation that Singapore has over Hongkong as a Fintech hub.

MAS have recently tied up with CFTC (Commodity Futures Trading Commission) in the US to share the findings of their Sandbox initiative. Such relationships between regulators help keep regulatory frameworks aligned across jurisdictions . So, when a Fintech is looking to expand beyond borders, they don’t have to rethink operational, strategic or technology aspects for the new jurisdiction and they can focus on what matters – the consumers.

As Fintech evolves over the next few years, there are several ways in which Banks, Insurance providers, asset managers and regulators can work in partnership with Regtech firms. In some areas, these firms will piggyback off what the incumbents have or haven’t done.

There is often a rule of thumb in the top consulting firms – build propositions in an area where there is fire. In other words, if a client has a major issue that could cost them money and/or reputation, come up with a solution for that. This is particularly true with Regtech firms, where they focus on an area that has a serious lack of control and governance.

However, in many parts of the world, there is a genuine opportunity for Regtechs to go a step further and define the controls in collaboration with the regulators, and perhaps ahead of the regulators.


Arunkumar Krishnakumar is a VC investor focusing on Inclusion, a writer and a speaker.

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