By Mark Somers, Technical Director at 4most Europe (www.4-most.co.uk)
William Coen, the Basel Committee Secretary General said in a recent interview in the Financial Times, that he believes that the avalanche of new risk regulation is coming to an end. Risk functions across financial services will no doubt be relieved. Certainly banks across the globe have been kept busy in recent years; increasing the capital supporting their operations by nearly $0.5 trillion since the credit crisis, implementing a new Capital Accord (Basel III), adopting forward looking credit loss accounting rules (IFRS9) and the introduction of annual stress tests to name a few. However, to assume this implies no further change is likely, would be wrong. The standards are set, the fires are largely out, now the work of implementation is in full swing and as usual the devil is in the detail.
Increasing use of mathematical models in risk
During the early phases of the economic crisis there were respected voices (for example Andrew Haldane at the BoE and Thomas Hoenig at the US Federal Deposit Insurance Corp) calling time on the complex mathematical models that in some instances, failed spectacularly and have often been viewed as being a key ingredient of the volatile and opaque nature of pre-2008 finance. Whilst backstops in the form of leverage ratios (a simple to calculate ratio of capital to the balance sheet) have been implemented as part of Basel III, the desire to go further – to put into effect a Luddite reversion to the days of manually-led risk decisions – has not caught on in banks and consequently regulators can’t ignore their complex models that guide important decisions either.
It remains true that banks that can use models effectively to measure risks and thereby provide better quantification of them to investors and regulators, will have a competitive advantage and will dominate their markets. Whilst BIS and other prudential regulators may have put less reliance on models for capital, the current introduction of IFRS9 into accounting principles will demand across the board, from the simplest mono-line mutual to more sophisticated international institutions, perhaps the most significant increase in risk model complexity of retail and commercial banks since the introduction of the Basel II internal ratings (IRB) system nearly 10 years ago.
Enhanced approaches to understand and manage model risk
With more reliance on risk models (not less),becoming inevitable then a more mature response to model complexity is to consider and formalise assessments of model risk. This is an area that is likely to mature considerably with the use of internal model “triangulation” and external benchmarking – using not just one but diverse independent models that have different assumptions to assess the uncertainty of potential outcomes. This is important to assess both the systematic model risk inherent in the regulatory RWA calculation within the Basel capital formulae and to understand model risks within internal bank models used in impairment,stress testing, pricing or credit decisions.
Another key element to manage model risk is to ensure a robust governance structure. This requires capturing expert judgement and insights from monitoring and back-testing to systematically review qualitative factors about models and aggregate these inputs to ensure models are working and used appropriately. Model governance provides the framework that should hold model builders to account – importantly, emphasis needs to shift from trying to simply guarantee that the single “best” model is used by the business to ensuring a range of model assumptions are considered and disagreement in model outputs are understood in the organisation rather than blindly following the number spat out of a process.
Increased sophistication in trying to understand stress scenarios; the interplay between economy, politics and future regulation
While economics remains a contentious discipline, economic history tells us unequivocally that economists are spectacularly bad at forecasting the timing of turning points of economic cycles. This is likely to be because at some level these phenomena exhibit a form of self-organised criticality – like the heap of sand in the bottom of an egg timer, the steepness of the slope is well defined but the timing and location of the next “sand slip” is not predictable. The same phenomena is present in many natural processes involving imperfect dynamic equilibria and economics is full of examples of such processes too.
Given the trigger of the next crisis may be unknowable then dealing with extreme events needs to focus on scenario analysis not backward looking historical models. To make this approach meaningful however many tens or hundreds of diverse scenarios need to be considered, their probabilities and severities assessed. This compares to the handful of relatively banal scenarios that regulators currently assess. To do this effectively will require new tools and a fresh approach.
About the author
Mark Somers is Technical Director at specialist analytics consultancy 4most Europe, based in London. The company provides a range of products and services across credit risk, fraud and marketing, working with blue chip clients predominantly in the banking, retail and mobile sectors.
About 4most Europe (www.4-most.co.uk)
4most Europe Ltd is a specialist credit risk analytics consultancy with offices in London and Edinburgh. The company provides a range of products and services across credit risk, fraud and pricing, working with blue chip clients predominantly in the retail banking and mobile sectors. The company offers a flexible, competitive model, either working with clients to manage regulatory change or delivering and implementing business critical solutions.
Why insurance needs Tesla’s autopilot too
By Christian Wiens, CEO of Getsafe
Digitization is the industrial revolution of the 21st century. What does this mean for a data-driven industry like insurance? The answer is simple: Turn everything on its head and reinvent yourself under high pressure- the future of insurance is digital.
“Hello Timo, nice to see you. I’ll be glad to help you.” Carla records claims 24 hours a day, seven days a week and takes less than two minutes to evaluate and process them. Carla works for a digital insurer and is a chatbot by profession. While she is answering Timo, she contacts the bank in the background, which pays Timo back his money – the same day. This is not a dream, but already reality.
In the digital age, intelligent machines are the new workers on the assembly line, and data is the new raw material. This applies to almost all industries and applies in particular to the insurance world as insurance is based on mathematical models and probability calculations – in short: on data. The more data on which the calculations are based, the easier it is to derive and price risk profiles. Data therefore changes the core of the product “insurance” in three essential areas; the offer phase, in the event of a claim and in the long-term customer relationship.
In the offer phase, we will experience long-term personalized product bundles that fit customer needs much better – away from standardized and inflexible policies. If the insurer can better assess the needs of the customer on the basis of his past history or behaviour, he is in a position to put together tailor-made insurance packages.
For example, it would be conceivable to automatically adjust the insurance cover as soon as the customer’s life changes, for example if the customer gets married, buys a car or a property or travels abroad.
Customer experience in the event of a claim will also change dramatically. Fraud is still the biggest problem in the system, with 2 percent of the customer base causing 40 percent of the system’s inefficiency. According to estimates by the Association of British Insurers (ABI), one insurance fraud is detected every minute – amounting to economic losses of £3bn every year. Of the estimated worth of total fraud cases a year, £2bn goes undetected.
But what if insurers are better able to assess customers on the basis of data and know which customers they can trust – and which not? Credible customers could then benefit from immediate payment of the loss incurred, while the few “black sheep” would not even be accepted as customers or would be checked more closely in the event of a claim being reported.
The computer does not act uncontrolled, but within certain parameters defined by humans. This is comparable to processes in the manufacturing industry: Here, too, people define the exact parameters that are to be checked – controls are implemented by machines that are significantly less prone to errors. The situation is similar when it comes to insurance fraud: people make value judgements and specify which indicators can point to a case of fraud. They retain sovereignty over the entire process. The smart algorithm, on the other hand, is only the tool for evaluating and linking the many individual data points. Smart algorithms will reduce employees’ workload, but will not replace them.
Finally, digitization will also change the long-term relationship between insurer and insured. Tomorrow’s insurance will not only settle claims, it could even prevent them arising. A better database will not only make it possible to calculate the probability and amount of loss more precisely, it will also make it easier to calculate the risk of loss. Digital systems and sensors can also help prevent possible claims. Telematic tariffs in motor vehicle insurance are already moving in this direction by promoting a prudent driving style.
Sensors on washing machines and industrial plants or intelligent smoke detectors are one thing – monitoring people in the health sector is another. Some health insurers reward sport activities, for example, if the customer can prove this with smart fitness watches. It remains to be seen to what extent customers are willing to exchange this personal data for premium refunds. In the long term, the legislator will also be asked to take action to ensure that the solidarity principle is not undermined.
However, the danger of increasing surveillance is countered by a clear increase in customer service, individualised services and flexibility on the customer side: Digital insurers rely on customer’s self-determination and a positive insurance experience in an industry that sometimes appears to be immobile and non-transparent.
Digitalisation has reached the insurance industry, but has not yet shaken its foundations. That will change: Tomorrow’s insurance will have little in common with today’s structures and processes. The autopilot at Tesla will also come for insurance. Not all companies will be able to master this switch to become digital insurers.
How ISO 20022 migration is changing the landscape in payments
By Paul Thomalla, Global Head of Payments at Finastra
The ISO 20022 standard is a catalyst for change in digitalisation and payments. The current edition of the standard was published in May 2013, and it’s been clear since then that the standard represents the future of payments messaging. This is due to the rich information, process automation and interoperability it enables. What started off in the Automated Clearing House world with the Single European Payments Area is increasingly becoming the de-facto standard for instant payments and for high-value payments worldwide. In fact, we estimate that all major payment systems and currencies will have moved over to ISO 20022 by the end of 2023.
Banks, meanwhile, will be able to get closer to their customers and offer better services. As this happens, the nature of the entire payments supply chain will change: there will be no one owner. Instead, consumers, corporates, banks, software vendors, fintechs and other stakeholders will all play a part.
Migration to ISO 20022 is moving at pace with one of two adoption models being taken. In the first approach, a ‘like-for-like’ migration occurs, which means data fields and messages are gradually moved over in compliance with the new ISO 20022 standard. However, the bank and client aren’t reaping the potential of the new standard as no further action has been taken. ‘Going native’ is the second approach. This allows extensive data sharing between banks and corporates unlocking a range of benefits including deeper insights into customers and partners, better accounting and financial data and more efficient payment processing. Data-rich messages can provide corporates with all the information they need to automatically reconcile transactions the moment they happen.
Banks deciding which way to move forward must remember that corporates have been waiting eight years for this new ISO 20022 functionality and if their bank is not able to deliver the promised benefits, they could decide to take their business elsewhere.
Planning the migration process
Deciding which approach to take is the first step in the migration process for banks. The main transition models being deployed to the market are: the ‘like-for-like’ translation model, or; for an ‘ISO-Native’ approach – either the complete overhaul model, or the hybrid model.
The translation model approach translates incoming MX messages to the SWIFT MT format and vice-versa for outgoing messages. This model is less disruptive and has a lower upfront cost. However, it involves high dependence on third parties resulting in less interoperability with fintechs and no new customer insight. The complete overhaul model allows organisations to execute a wholesale architecture transformation. This approach gives access to leverage rich data across the business including new insights on the market and customers. One negative aspect of this approach is the fact it is disruptive and requires a large upfront investment. Finally, the hybrid model works well for global banks where translation is needed across the board. This approach offers flexibility and the ability to localise strategic response, however it adds a level of complexity to users. The leading model is unclear, but banks must remember to align their payments operations with their chosen model.
That’s not to say that the adoption of ISO 20022 will be plain sailing. One challenge is that the standard describes an asynchronous messaging process. For banks which currently rely on return messages to confirm the successful completion of a payment transaction, this will cause significant upheaval, and is a change that underscores the need for everyone in the payments ecosystem to get ISO 20022 migration right. Banks will need to overhaul their business processes and operations to adapt to asynchronous messaging. This will in turn require new systems, such as Confirmation of Payee and Request to Pay.
The new format requires a fundamental change to the payments world, so the decision on which transition model best suits their needs isn’t to be taken lightly. Internal and external considerations will help banks determine next steps to successfully implementing ISO 20022. Internally, banks must ensure they have the right people to deliver this transformation, have processes in place to easily review and adapt back office functions and have the correct technology required for the migration. Our approach at Finastra has been to build a payments hub that is ISO 20022 native from the start – ready for widespread adoption across the industry. Banks must also look at external factors like customer impact, market share, competitors and regulatory constraints.
Benefits across the payments value chain
The adoption of ISO 20022 allows for additional, enriched data to be transferred within the payment instruction. The new format has more granular and better organised data elements as well as a consistent data dictionary across the payments chain to speed processing and improve compliance. This prevents misinterpretation and expensive manual interventions. All of this will facilitate improved processing and allow all agents in the payment to make more informed compliance decisions.
In the short term, including additional party and remittance information will help reconcile transactions. For example, QR codes are being used more widely on invoices, clearly identifying the beneficiary and facilitating automation in the back office. Looking at the medium term, institutions will be able to limit the resources they have to dedicate to exception handling and one-off investigations due to missing information or unstructured input that cannot be easily integrated into automated workflows. And finally, the benefits of ISO 20022 in the long term mean data that is properly structured and adhered to will support better regulatory compliance practices and financial crime monitoring.
The rewards of ISO 20022 make any temporary disruption more than worth it. We’re excited to enter a new era of payments messaging that will drive collaboration, innovation and efficiency through interlinked partner ecosystems.
Agile thinking in times of uncertainty
By Caryn Skinner, Co-Director of Sharpstone Skinner
“Several times lately, I have finished my work, closed the laptop and sat staring out of the window of my spare room office worrying that I don’t have the answers. That my team are looking to me for guidance about the future…and I simply don’t know.” Paul Jackson-Cole, Executive Director of Engagement, Parkinson’s UK
A genuine, honest reflection from an impressive and successful leader. He has gravitas, is trusted and a great coach to his senior reports. He is also highly intuitive, with an innate ability to be a pioneering visionary who can then work with others to ground that vision into reality. And yet, he is stuck. He still has his instincts, yet with the world, in flux, he is finding it hard to convince his team to go with him because they need more tangible evidence to ground his ideas.
Gut-feel judgement is part of agile thinking which is a crucial leadership skill. In the financial world you may have finely honed other types of thinking as you need to show evidence, use data and put forward your thoughts in a rational way.
Agile thinking has five main features:
Systems thinking – investigating an issue from a broad perspective to understand the interdependencies
Possibility thinking – to be open-minded and generate a wide range of possibilities, the classic brainstorm
Logical analysis – to reach valid conclusions using clear, rational logic
Evidence-based thinking – identify core issues by analysing evidence from relevant resources
The fifth one is gut-feel judgement – relying on your gut instincts to provide valuable input for decisions.
Richard Branson says, “I rely far more on gut instinct than researching huge amounts of statistics”, and he’s not done too badly.
Mr Branson may make you shudder though, as it is quite an extreme view. Most of us use all or a few of them combined. Yet in this world of unknowns, your instincts may need to be more finely tuned. It isn’t easy to find evidence and interdependencies if we have never been in this situation before. Rational logic needs something tangible to test it against, the world feels nebulous at the moment. Being open-minded looks like a good option yet can get stifled because the possibilities are almost endless.
Here are some ways to tap into and use your gut-feel judgement:
- Know that your instincts are not woolly ideas but based on your years of experience. The thought has come from somewhere, an experience you have had, something you have read a conversation you had with a colleague.
- Feed and grow your instincts. The more exposure you have to your market the harder your instincts will work. Keep getting out and about, visit your people, talk to them, learn from them about the front-line challenges and successes.
- See your business through the eyes of your customer or client. Why do they like doing business with you, what would they like you to do better and does your business align with their needs.
Make your own observations about what’s next for your business rather than staring at spreadsheets of cold data. I heard about a trader who regularly walks the shops to see what’s selling and what isn’t, it informed her instinct about where the next investments might be.
- Keep in touch with the world around you, tune into what’s coming over the horizon. A client of ours was in marketing for a bank, he regularly spoke to his teenage nieces and nephews about how they communicated, how many digital “languages” they spoke and which social platform they used for what. They were his future customers and the conversations fuelled his instincts in discussions with the senior team around the bank going online and changing the way they communicated with customers.
- Trust your gut then test it against other types of thinking to ground it and help you sell it in. Others may not get your vision so painting the picture for them with more solid evidence will make your job easier.
It is an exciting area of leadership and one that, perhaps, has been overlooked in a world that can access evidence, stats and data at the swipe of a screen.
Next time you find yourself staring out of your home office window, let your thoughts wander, don’t evaluate them or crush any ideas that come to you, it might be that your gut is trying to tell you something.
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