Blockchain technology is gaining traction amongst mainstream financial service providers as the technology guarantees security, decentralization, cheaper transaction fees and faster ways of sending funds across the globe. Mastercard has also not been left behind in these developments. This is after it was rumored as being one of the payment card providers that refused to support Litecoin (LTC) and its .
a blockchain system to store and verify identity data. The patent application was published on the 12th of April this year. In the abstract of the patent application, the team at Mastercard describes the patent in its intricacies. The team states that traditionally, proof of identy had been provided via government identification, credit cards and business cards and that such proof may be inaccurate or fabricated through fraud. They propose that there is a need for a technical solution to provide for the immutable storage of identity and credential data in a secure and verifiable manner.
This news is less than one month after Mastercard declared unless they were backed by a Central Government somewhere in the globe. Ajay Banga, the CEO of Mastercard had been quoted as labelling all other cryptocurrencies without government backing as being ‘junk’.
The patent application proves that most financial service providers back the technology of blockchain but still continue to be against the current and major cryptocurrencies in the market. Perhaps this is a case where the big financial institutions are a bit afraid of the potential of crypto, that they want to talk ill about them. They would then proceed in launching their own technology on the blockchain and marketing it to the masses.
Litepay was a good idea and had planned to revolutionize crypto by allowing owners to use them for day to day transactions like the regular fiat and debit or credit cards. Users were to load their LTC onto the Litepay cards and use them for regular transactions. The currency conversions were to be done by the service in the back-end software. Price predictions before the Litepay cancellation had even put LTC at $1,000 by the end of the year.
[Photo source, PYMNTS.com]
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With growing interest from clients globally, Redsand Labs is rising to the challenge and opportunity of developing innovative solutions using AI for an array of complex problems in financial services. In this article we take the time to highlight just 2 of our success stories.
1. Legacy technology — often unavoidable but the constant impediment to partner and customer engagement
In building an Open API offering for a large bank, we discovered that providing API’s are the easy part. Engaging with and validating potential partners who will interface with the Open API is far more complex.
Financial Services organizations have, over many years, developed on-boarding processes and conditions for providing traditional services to customers.
For instance, opening a new business account with a bank requires a prospective customer to submit a set of supporting documents and provided these are correct and compliant, the service is provided.
In the case of a prospective new FinTech wanting to interface with a bank through its Open-API framework, the nature of the relationship is very different and the existing on-boarding channels are not able to evaluate the suitability of the FinTech. This engagement requires a more complex conversation with someone who is skilled in a number of disciplines ranging from the ability to evaluate the FinTechs’ business model right through to understanding alignment with the strategy of the organization.
These skills are rare and hence a complex face to face conversation with each prospective new FinTech partner/customer is simply not feasible or scalable.
‘Ai?’ the ‘Ah-Ha’
Redsand Labs set about to solve this for a client. The solution took the form of an AI product build to conduct the complex conversation either through a chat or voice interface.
Through this process, we learnt that this model could be applied to many other areas from a complex loan application to interrogating investment choices.
A major benefit to using AI in a B2C context, was that customers felt much more at ease posing what they considered “stupid question” to a voice based chatbot, without fear of judgement.
We learnt about the “fear of judgement” element some months earlier when we built a retail banking prototype using Amazon Echo as the interface and had the best response from unsophisticated people who feared being patronized by bank staff to elderly people who felt more at ease engaging through a voice interface to execute regular payments and obtain account balances than using a web or mobile device.
2. Customer contact — The potential to deploy AI at scale
In another example we engaged a large retail bank to develop a full-blown AI powered chatbot to manage the typical call centre functions. The purpose was primarily informational with limited transactional capability.
Teaching machines
Soon we discovered that there is no such thing as a gold standard in banking AI systems. There is a fair amount of AI technology being offered, but many are merely engines and finding and structuring the data in order to “teach” the system is what takes real skill and effort. And this was an absolute requirement.
In addition, despite the advances in call centre voice logging systems, it was very difficult to find a history of the questions customers had asked the call centre agents. Internal classifications were internal and did not resemble real questions customers asked. Furthermore, regional colloquialisms were freely used by customers in both voice and text messages and the machine had to “learn” these.
Bank speak and alienating customers
Probably the most astounding insight we gained was that the information supplied on most retail banking websites in the form of product information and FAQ’s were completely out of sync with the actual questions customers were asking. People developing content for the websites are typically well versed in “bank speak”, but not in “customer speak”. It’s probably fair to say that only bankers really understand the information provided to customers.
Redsand Labs Ochestration Engine (OE) — the true enabler
Based on these learnings, and especially the fact that customers expect a chatbot to know everything in the organisation, we completely reworked our initial system design. We still used a highly rated off-the-shelf NLP engine, but built a customer focussed engagement layer which could span the entire organisation. We deployed our open API OE (orchestration engine) to tie all the existing legacy verticals together — this was the missing ingredient in the initial design.
It sounds simple, but without an orchestration layer, each business vertical will need to implement and maintain a chatbot instance — which is pretty much how call centres operate!
The result is a completely new customer engagement channel which was recently launched to all internal staff with full external launch scheduled for May 2018.
At Redsand Labs our objective is to solve real problems and develop systems which make a big difference in many peoples’ lives.
We have a vast array of technologies and technology partners at our disposal. However, to solve real problems and build systems people want and need, we are obsessed with applying our discovery and analytical capabilities before we touch any technology.
We have a track record of very innovate apps and solutions, but all these successes started with deeply understand the problem we are solving.
©Redsand Partners is an innovation and venture practice aimed at emerging technologies and markets to supporting investment due diligence, venture building and specialised innovation advisory. Redsand Lab is our technology development capability.
John Campbell, Partner, Investment & Venture Building at Redsand Partners
