Fraudsters are profiting from the pandemic, while financial firms’ fraud-detection systems are swamped with false positives. As firms adjust to a new ‘normal’, graph analytics and supervised and unsupervised models can help them keep pace with criminal behavior.
Extreme weather makes forecasting and quantifying insurance losses harder,
and ‘cat’ models struggle to predict events more extreme than those in the past. As
regulators demand more action, dynamic ‘earth system’ models offer a better way to
anticipate more climate-change risks.
Failing to incorporate renewable energy sources effectively into power
networks can create serious issues around energy pricing and forecasting. Some neural
networks can mitigate renewables’ intermittency, but require the right expertise and data.
Now that Big Data is mainstream, model developers face an epistemic
trade-off: enable models to make more accurate predictions by loosening traditional
statistical methodologies. But what impact might this have on the future accountability of
our financial models?
Biometric technology can enhance fraud and anti-money laundering
processes, but can carry big risks. As it becomes more widespread, financial firms and tech
vendors must develop the security and governance frameworks to realize its potential –
before regulators force them to.
The ability to distribute trustworthy credit is a societal cornerstone.
But what happens when traditional credit scoring methodologies aren’t available? Will new
'advanced' credit models in emerging markets be self-fulfilling prophecies?
Developing AI algorithms without strict definitions could create ethical
problems for financial firms. To avoid mishandling their algorithms and potentially harming
certain customer groups, firms must ensure their AI tools are no broader than the
definitions they are based on.
New ways to capture and package previously inaccessible data have given
financial institutions (FIs) a diverse set of methods with which to assess the
creditworthiness of corporate and retail customers. Despite the appeal, however, deploying
this data does not guarantee clearer credit risk assessments – instead, it may muddy already
Thanks to a booming payments market, the amount of transaction data is
growing – as is its value. But regulation around it is patchy at best, and as more
transaction data is used to feed models and analysis, more transparency and clarification
around its use and abuse are needed.
The speed at which the liquified natural gas (LNG) market is maturing has
created inconsistencies in how LNG is priced – not least in Asia, where growth is fastest.
The obvious but untested solution – an Asian pricing hub – will take time to develop, but in
the meantime benchmark assessments could offer a viable alternative to existing pricing
The IASB issued IFRS 17 in a bid to standardize insurance contract
accounting, but reinsurance firms, because of their particular idiosyncrasies, will struggle
to comply. Unless the IASB makes significant modifications to the standard, reinsurers
everywhere will have to reassess the nature of their life insurance contracts.
Many projects labeled ‘private blockchains’ are merely database hygiene or
‘permissioned DLT’ solutions given a more marketing-friendly moniker. But increasing misuse
of the term ‘private blockchain’ could create confusion in the market and undermine a clear
understanding of blockchain’s real strengths in specific use cases.
Impending regulation affecting shipping fuel will reduce atmospheric
pollution but send shockwaves through the shipping and oil industries. Market players have
several strategic options, but uncertainty clouds their choices. To what extent is the
industry able to adapt, and what will be the likely cost of compliance?
As fear around cyberattacks grows, so-called ‘bug bounties’ offer firms an
opportunity to buy information on security vulnerabilities in their systems before they
become public or fall into the hands of bad actors. In future these transactions will be
moderated by trusted intermediaries; until then firms should carefully weigh up their pros
In striving for growth, many FinTech firms enter different markets with
solutions that use the same underlying technology. As highlighted by recent AML-related
issues, however, this technology is seldom regulation-friendly. To withstand deeper inbound
regulatory scrutiny, FinTechs must adopt robust risk technology.
Tied to the growing popularity of machine learning (ML) tools is the need
to explain their underlying rationale. But buzzwords, like ‘glass box’, are steering the
explainability conversation off course. Meanwhile, without proper investment in the tech
innovations and governance methods to properly validate ML, it could proliferate throughout
the financial industry without the necessary safeguards.
A relative lull in transformative IFRS-related system implementations
means that financial firms can relax a little. But not too much: now is the time to work
strategically with other players to create real value from IFRS compliance.
Advanced analytics and AI promise revolutions everywhere, but real-world
constraints abound. This is notably true in the world of credit scoring, which needs to be
understandable, is often slow to give out real-world results, and can be muddied by the
addition of unnecessary factors.
A lawsuit against Google’s parent Alphabet threatens broader data
security. Regulators should provide clarity on breach disclosure timelines; financial
services institutions and suppliers should welcome it.