Panelists below from left to right.
Irina Samoylova Kunces – Regional Head of Financial Markets, Corporate Finance, and Segments—FCC Advisory, Standard Chartered Bank
Vikas Agarwal – Financial Crime Technology and Analytics Leader, PwC
Angus Grandfield - Americas Head of Product, Segment and Training at Standard Chartered Bank
Delia Pawelke – Financial Crimes and OFAC Manager, Stripe
Kevin Bogdanov – Director, Market Development: Client and Third Party Risk, Thomson Reuters
Embracing FinTech Disruption
We are likely to see a gradual increase in fintech adoption over the coming months, as banks begin to plug the gap between recognizing the importance of technology and actually incorporating it into their business models.. At the same time, those banks that do already have a system in place to support fintech adoption – or have already set out on the path to instating one – are in a good position with regards to what will be an increasingly vital and defining aspect of the future of the industry. The challenge for many financial institutions is determining the best way to embrace the fintech imperative given their unique strategic vision and business objectives. What we learned is that there is no single best way to approach fintech. Leading financial institutions are pursuing various avenues to leverage fintech – from partnering and buying to sourcing and direct investment. Yet, one best practice underpinning a successful approach to fintech, is the definition of a focused fintech strategy, which includes:
Solid knowledge of current business operations;
Awareness and early identification of signals of change;
Readiness for change and understanding of cultural barriers;
Alignment between business objectives, risk appetite, and FinTech approach, and;
Broad array of innovation that includes both incremental improvements and transformative change.
What does it mean to bank FinTechs?
FinTech is an amalgamation of the words “financial” and “technology.” It refers to the use of new technologies in the financial services industry to improve operational and customer engagement capabilities by leveraging analytics, data management, and digital functions. FinTechs increasingly recognize the significant costs of customer acquisition in financial services and barriers to cross-border business that banks are well-equipped to bridge. Furthermore, more emerging fintechs recognize the opportunity to have a role as part of a broader banking ecosystem, developing technology that can help transform an industry and payments infrastructure, not just support one bank.
Current Environment: FinTech
It is clear that the digital revolution in financial services is under way, but the impact on current banking players is not as well defined. Digital disruption has the potential to shrink the role and relevance of today’s banks, and simultaneously help them create better, faster, cheaper services that make them an even more essential part of everyday life for individuals.The concern is that established financial services players are not doing enough to keep up to speed with this surge in new innovation investment. Legacy technology and the difficulty of deploying new technology fast is a big part of this issue.
Blockchain and Cryptocurrency: Risk Management
Businesses are increasingly coming into a new form of data storage and transmission known as blockchain. Blockchain technology has a wide array of applications, but probably the most high-profile one is the relatively new world of cryptocurrencies. While blockchain, due to its distributed nature, looks promising to reduce the risk inherent in current systems, nonetheless it will surely introduce a new set of its own risks. And while it is not possible to anticipate all risks, here are a few that are readily foreseeable:
There will be no intermediaries to handle counterparty risk and dispute resolution;
The absence of an intermediary controller means that theft or accidental loss of private “keys” (digital account details) could cause irretrievable loss;
Sourcing the blockchain technology to vendors will result in significant third-party risk exposure, and;
Regulatory requirements are still in flux.
Consumer Usage of FinTech:
2015: 15 %
Application of Machine Learning
Machine Learning is a sub-field of Artificial Intelligence where computers are able to learn when exposed to new data without being explicitly programmed. The potential applications for this within suspicious activity monitoring and transaction monitoring are vast. Delia Pawelke stated that “machine learning is everywhere. When you go on Amazon and they suggest things for you to buy, that's machine learning. When you're on Google or Facebook and there are ads eerily similar to what you've been typing in email, that's machine learning.”
Regulation technology (RegTech) is the branch of fintech that focuses on improving financial services providers' compliance and internal control systems. Among other things, regtech applications automate risk management processes, facilitate regulatory reporting, prevent fraud, enable companies to stay abreast of regulatory changes around the world, and support strategic planning.Vikas Agarwal stated that “it is companies trying to really focus on automation, trying to focus on artificial intelligence, trying to take blockchain technologies, and they're trying to say - how can we think about compliance in a different way, go past rules based monitoring.”
In all areas of the financial industry, technology innovation is transforming the payments landscape with new capabilities and developments merging - that enable transactions to occur with greater ease, efficiency, and transparency. In many countries across the globe, domestic payments have gone—or are in the process of going—real-time and mobile. This is not only altering the payments process; it is fundamentally altering the behavior of consumers in terms of how and when they choose to initiate payments.
Limitation of Rule-Based Monitoring
The problem with rule-based analytics is that every rule is set to detect only a specific behavior (e.g. structuring) and has to achieve this with a high level of accuracy. Much time and effort is put into configuring this rule and fine-tuning it to achieve the desired performance. If this particular event (e.g. structuring) does not occur, then the rule is basically dormant, doing nothing, and producing no value. To make matters worse, it is often inappropriately applied, producing false alarms instead of positive detections, adding to the frustration of the users. Therefore, rule-based systems suffer many shortcomings:
Only capable of detecting simple known behaviors;
Not possible to specify all rule combinations;
Limited coverage; Inappropriate use leads to high false alarm rates;
Some scenarios are difficult to specify any rules;
Time consuming in rule configurations, and;
A new rule/template has to be defined for each new behavior to be detected.