Lessons Learnt from Covid-19 ... or Not?

Covid-19 is a health crisis, a business crisis and an economic crisis which has struck the insurance industry hard.

Claims spiked in some areas while volatile financial markets made it almost impossible to steer the investment portfolio, and lockdown measures kept staff at home while struggling to cope with surging call and claim volumes. Meanwhile, there is vocal pressure from some quarters for a “flexible” approach to claims, where “flexible” is shorthand for dishing out large amounts of money for claims which may or may not be covered.  

How has the industry coped, and what lessons has it learned?

To answer that question, Crescendo Advisors carried out a series of structured interviews with a selection of risk and finance professionals from insurance firms. Most of the firms were UK based, with an aggregate turnover of £120 billion in 2019.

Although the firms varied in size and portfolio mix, there was a high degree of consensus in their opinions. Here are Crescendo’s top five findings and conclusions:

• While most UK firms have weathered the crisis to date, it appears that few did so as laid out in their pre-Covid-19 business continuity planning.  Business continuity plans usually assumed local outbreaks and had to be re-created in the face of a total and global shutdown.

• All firms who viewed their lockdown experience as ‘successful’ attributed that to excellent, ongoing communication from senior management to all stakeholders;

• The traditional hostility to staff working from home has changed from “not possible” to “why not?”. Going forward firms expect staff to continue working at least part-time from home, and hence plan on reductions in their office footprint;

• As remote working and virtual teams have become the post-Covid vogue, the purpose and value of The Office is being critically re-evaluated. It may still be the best place for meetings and staff onboarding, but do we really need all those desks crowded together?

• With staff working remotely, the cost-benefit dynamic of outsourcing could be changed so that firms will find it beneficial and desirable to bring activities back in-house.

Interestingly, while most participants anticipated the need for a lessons learnt exercise, only one of them acknowledged at the time that his firm was already kicking off such an exercise.

Are insurers perhaps being complacent? They had six weeks to prepare for lockdown and they put the time to good use. By the time staff were required to stay home, many did so with newly acquired laptops and secure connections. The main limitations on productivity came from the lack of suitable home office facilities or from inadequate broadband speeds. The show stayed on the road with remarkably few wobbles.

Next year UK insurers are likely to work in the implementation of operational resilience requirements.  There are lessons to be learnt from Covid-19.  But here’s a thought, if working from home is no longer the backup disaster recovery plan – it is the new normal – what is the new disaster recovery plan?

This post has been written by Isaac Alfon (Managing Director) and Shirley Beglinger (Advisory Board Member) at Crescendo Advisors.  

Crescendo Advisors (www.crescendo-erm.com) is a boutique risk management consultancy.  We would be happy to share an overview of the findings of this survey.  We can also support your efforts to both learn lessons from Covid-19 using the tools we developed for this survey and consider the implications of working from home arrangements for the risk and control environment.

Delegating Decision Making to AI Tools – Choices and Consequences*

Sometimes when I hear about Artificial Intelligence (AI) tools it seems like it is all about the technical details of the model and the data, which is certainly very important. This post is about another important aspect: the operating model in which the AI tool will operate.

There are many aspects of such an operating model.  Some are practical, such as ensuring that the tools integrate with other parts of the business.   In this post, I am focusing on the delegation of decision making to the AI tool – the choices that exist in most cases and the implications for the control environment.  These are summarised in the figure below.

At one extreme of the delegation of decision making, you have AI tools that operate independently of human intervention.  An example is algorithmic trading or an automated trading system which trade without any human intervention to use the speed and data processing advantages that computers have over a human trader.  Interestingly, this also represents one of the few prescriptive examples of PRA intervention where it requires that a human has the possibility of stopping the trading system.^[1]

At the other end of the spectrum, there are AI tools used by experts in a professional environment.  For example, actuaries might use machine learning techniques to undertake experience analysis and support reserving work.

Between these two examples, you have AI tools that provide a forecast or recommendation for consideration by an analyst.  For example, the AI tool could provide a credit rating that validates a rating derived using more traditional methods.

Another middle of the road alternative is ‘management by exception’.  This means that the AI tools have a degree of autonomy to operate within a ‘norm’, which is inferred from historical data.  Cases that are outside the norm are then referred to an analyst for consideration to improve and verify the predictions. 

These are business choices and in turn have implications for the development process of AI tools.   You would expect controls around data and model documentation in all cases.  But broadly speaking you would also expect a tighter control and a more intense validation for AI tools that operate more independently of human intervention.  This includes the depth of model’s understanding, including:

• explainability – why did the model do that;
• transparency – how does the model work;
• the impact on customers – e.g., the difference between Netflix recommendations and credit card underwriting.

The choices of operating model also have important implications for staff training.  AI tools operated by staff that have not been involved in its development must be trained to the appropriate level to ensure that the AI tool operates effectively.  For example, where ‘management by exception’ is adopted, staff would need the appropriate knowledge and skills to deal with the exceptions.

There are important choices for the operating model into which AI tools are deployed.  These choices have risk management and control implications and these choices may change over time.  An AI tool might start operating in an advisory capacity.  As trust in the AI tool increases then the delegated decision making can be increased.

These implications and choices should be considered as part of the model design.

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*  This post is based on my contribution to a virtual panel discussion organised by ActuarTech on AI Governance & Risk Management.

[1] Prudential Regulation Authority (PRA), Algorithmic trading, Supervisory Statement, 5/18, June 2018.

AI and Risk Management

Earlier this year, I gave a presentation to a group of actuaries - the Network of Consulting Actuaries - on the challenge of adopting Artificial Intelligence tools in Financial Services and how risk management help.  I have transformed the speaking notes into a paper - here.  

Happy reading!

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3+1 Types of Digital Transformations and How to Prioritize Them

A former insurance CEO once said that if you want to understand risk in financial services, you should start by looking at the products you are offering. I have been exploring how incumbents in financial services, and specifically risk management, should change to embrace FinTech. Inevitably then the subject of ‘digital transformation’ comes up. I have been speaking with various colleagues and friends recently and I realised that there are rather different forms of digital transformations with different implications for risk management and the business.  Here is my take on the various types. 

1.       Data-driven

Someone in the business takes the initiative and starts collating, curating and using the many data sources in the business to address specific analytical issues and enhance the quality of decision making.  This represents a bottom-up transformation with potential transformational features. 

In this case, buy-in is unlikely to be an issue. The main risk management challenge may arise from the scaling up of this initiative. For example, scaling up may involve using external data rather than internal data or bringing new technology to store the data, e.g. a data lake, which needs to be integrated into existing systems. It is also important that the consideration of analytical issues in the business factors in the need to maintain (and enhance, where necessary) an understanding of the risk profile of the business. For example, if additional data allows the business to modify its underwriting approach in a significant way, you should also consider how the (different) exposures would be monitored. There are a couple of examples here.

2.       Enhancing Customer Journeys 

This can be about how customers are serviced, given their existing journeys, and might include enhancing the front-end applications or rolling out new IT equipment to service customers. Alternatively, the transformation may be about changing or enhancing aspects of customer journeys. This might include, for example, introducing chat-bots as part of customer journeys (e.g. claims management) or applying an artificial intelligence-based tool to a specific process (e.g. underwriting).

This type of transformation has become the most visible form of digital transformation thanks to the various accelerators that incumbents in financial services have created. The challenge of buy-in is typically addressed by specifying that the accelerator should partner external providers with business leaders for whom the technology may be relevant. The impact on the risk profile of the business is also dependent on the specific transformation and should be considered from the outset. 

3.       IT-enabler

There are cases where the legacy systems become the main challenge and where the adoption of cloud-based services can be part of the answer. There are several approaches here, ranging from incremental steps to a ‘big-bang’ approach. One interesting idea is focusing on reducing the functionality of the legacy system and replicating that outside using new technology. 

These transformations may be motivated by concerns about operational resilience in the short term but might also support the transformations outlined above and enable more effective risk management. 

4.       Digital ‘Non-transformation’

This involves applying new technologies in the context of a new product line where there is no transformation as such. This clearly avoids the transformation in the short term but it can also provide the business with the means to build confidence in specific technologies (AI, blockchain) and the capability to execute and bring on board new technologies.

These types of digital transformations are not mutually exclusive, but it is important to be clear that they are different. Equally, they are not substitutes for each other and the real challenge is prioritising between them. This will inevitably vary between businesses, though I believe that there are standard considerations shaping the priorities such as the need to change the culture in order to mobilize the business for the digital era and the state of the core IT infrastructure, including the need to leverage technology as an enabler.  

What do you think about these categories? 

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The New and the Old in Risk Management

I have been writing about the new and the old in risk management over the past year. This starts with the slow pace of adoption of FinTech by incumbents in financial services. I have suggested that an important component of the change needed includes incumbents amending and enhancing risk management frameworks to reflect new FinTech innovations. (See my last post on the subject.)

Recently, I came across an article from McKinsey that makes a similar point in the context of model risk and the adoption of artificial intelligence (AI) and machine learning. It turns out I am in good company! 

McKinsey’s article notes that banks have developed and implemented frameworks to manage model risk, including model validation reflecting specific regulatory frameworks, in this case from the US Federal Reserve (here). They recognise that the implementation of these frameworks is not appropriate to deal with the model risk associated with AI and machine learning. Banks are therefore proceeding cautiously and slowly introducing new modelling approaches even when these are available.

The article then shows how a standard framework for model risk management is used to identify extra considerations required for this framework to cover appropriately AI and machine learning models.  The key message is that the challenge of adopting AI and machine learning can be addressed through a careful consideration of existing approaches. 

Two further thoughts from McKinsey’s article. Firstly, the article rightly refers to model management rather than validation. It is always useful to reiterate that model validation undertaken by the risk function is just a component of how models are managed in the business. Secondly, model management should not apply only to internal models used to calculate regulatory capital, but should apply more widely to models used in the business such as those used for pricing, valuation of assets and liabilities.

The article ends with a cautionary tale of an unnamed bank where the model risk management function took initial steps to ready itself for machine learning models on the assumption that there were none in the bank. It then discovered that an innovation function had been established and was developing models for fraud detection and cybersecurity.

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The Curse of Risk Appetite

In this post, I go back to one of the fundamental aspects of an ERM framework: risk appetite. ‘The Curse of Risk Appetite’ is part of the title of an interesting paper reviewing the misuses of risk appetite.[1] Some of the misuses described in the paper might sound familiar, but perhaps the key point to take away from the paper is that there is a potential for risk appetite to become synonymous with ‘a consideration of risk’. I am not sure this was ever the intention. 

The paper includes several useful suggestions to enhance risk appetite. They are focused on the long-run value of the firm and on the structure of risk appetite statements, reflecting a view that risk is the likelihood of falling below critical levels of performance. However, my attention was really caught by the authors’ suggestion to improve the organisational process for risk management. They suggest that a risk function’s role should be defined to include responsibility for evaluating the combined effect of strategic initiatives and capital budgeting on the firm’s overall risk profile.

On one level, this prescription is consistent with the view that the aim of the risk function should be to ‘protect and enable’, with the emphasis on the ‘enable’ aspect which sometimes gets overshadowed by ‘protect’. I am attracted to this suggestion because it turns a vision into a practical requirement that can be incorporated into an articulation of roles and responsibilities for a CRO or risk function. 

If, however, this was implemented literally in UK financial services, I suspect there would be an issue with regulators’ expectation about the independence of the risk function (second line of defence) from the business (first line). 

A similar outcome could be reached by clarifying that the role of the CRO/risk function includes providing a risk opinion in the early stages of the consideration of major strategic initiatives that have the potential to alter the business’s risk profile. The emphasis on timing is important. Providing a risk opinion only when major strategic initiatives are presented for approval is unlikely to add value. A CRO/risk function opinion in the early stages is likely to support consideration of the details of the initiatives and how they can be shaped to strike the appropriate balance between risk and return.

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[1] Alviniussen, Alf and Jankensgård, Håkan, The Risk-Return Tradeoff: A Six-Step Guide to Ending the Curse of Risk Appetite (May 7, 2018). 

Risk Management as Infrastructure for Artificial Intelligence and FinTech

During 2018, I wrote several posts about FinTech, Artificial Intelligence (AI) and risk management.  I was kindly invited to present to the Network of Consulting Actuaries, I chose to use this opportunity to consolidate my views on the subject.  

There were several ideas flowing through my mind.

Firstly, informal evidence suggests that, for all the hype, FinTech and AI have not yet become mainstream in insurance or in financial services more generally.

Secondly, the largest business transformation arising from FinTech and AI is the adoption of these technologies by incumbents.  Indeed, I explored this in the context of banking through the group project at the Oxford FinTech Programme I completed in December 2018.

Thirdly, someone who works for a multinational insurer made the observation during an InsurTech event in London that as a regulated entity, the insurer has responsibilities and obligations towards their customers and must follow due process before they roll out new technologies.  There was a hint of an apology in this observation to the nimble start-ups in the audience.

Putting all these thoughts together led me to see the main challenge to the adoption of FinTech by incumbents as governance, including how risk management is applied in practice.  If the aim of risk management is to ‘protect’ or block, then the incumbent does not have an obvious lever to support the introduction of AI tools and FinTech.  

If, on the other hand, the aim of risk management is perceived as to ‘protect and enable’, then risk management can be part of the solution.  Risk management can lead to the creation of necessary infrastructure to ensure that AI tools achieve their transformational potential.  This includes articulating a vision of how a control framework should be leveraged, considering the impact of FinTech and AI on risk management frameworks, focusing on explainable AI, and articulating the implications for the target operating model.  This will facilitate incumbents’ adoption of FinTech and AI.  

Take a look at the presentation I gave (here) for a more detailed articulation of these points.

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This Time is Different - The Digital Revolution

The August issue of Central Banking, a journal, includes my review of a book about the digital revolution by Chris Skinner.  It is a fascinating book that can change pre-determined views.  You can read the review here or below.

This Time Is Different: A book review of Digital Human by Chris Skinner, Marshall Cavendish (2018)

Mr Skinner has written two books on FinTech and banking (Digital Bank and ValueWeb), and now Digital Human is his third. This represents an opportunity to take a step back and consider some of the bigger questions about FinTech. How much of a change could this represent for banking? For financial services? For society?

His main argument is that digitalisation has reduced the cost associated with a minimum viable product beyond recognition for nearly anything in financial services. One way of looking at FinTech is as ‘one big bucket of finance and technology’ with a range of technologies from InsurTech (based on artificial intelligence) to digital currencies, with mobile wallets and peer-to-peer lending in between. Indeed, one could make the argument that it should be called ‘TechFin’ instead. However, it is possible to make overall sense of these technologies by distinguishing between those that challenge existing business structures and those that create new ones. 

One of the main aspects of the digital revolution with respect to banking is the differential effect between the (developed) West and developing world. Surprisingly, it is not in the direction you might expect. For the West overall, FinTech represents a challenge to existing business structures. Current IT systems took shape in the 1970s and 1980s at a time when now-ubiquitous ATMs were first introduced. While the front-ends of these systems have changed over time, the core architecture has not. As Mr Skinner points out, CEOs invest significantly in systems maintenance to pass on to the next CEO rather than overhauling technology. I was left wondering if this might also be a reflection of misplaced risk aversion that contributes to the relatively short tenure of CEOs.

There also seems to be a potentially systemic issue arising from the natural ageing process of the programmers who can still write code in the language of the legacy systems (COBOL). Mr Skinner observes that more than 50% of COBOL programmers are over 45 years old, so the challenge of maintaining legacy systems is not going to get any easier.

However, the real challenge does not seem to be adopting new technologies but the vertically integrated business model of banking or, as Mr Skinner puts it rather eloquently, being ‘control freaks in a proprietary operation building everything themselves’.  As usual, technology enables the challenge but does not help the incumbent figure out how the business model should evolve and how to remain profitable. Mr Skinner offers two suggestions. The first is leveraging on its capital, history and brands and repositioning the business as a trusted party that can select specialised providers, like Amazon Marketplace. The second is leveraging on the data and focusing on advice and data analytics.

Indeed, there seems to be a change in emphasis in FinTech. Between 2010 and 2014, the focus was on disrupting existing banking business models and unbundling. Since 2014, the focus has shifted to collaboration with more dynamic banks leveraging on their customers’ reach and capital.

Perhaps the key point to emphasise is that the regulatory framework has already adapted to some extent, at least in the EU where Open Banking is already a reality because of EU directives.

If you don’t work full time in FinTech, it is difficult to form an impression about how far these trends could go.  (Yes, I know there are forecasts, but they are merely forecasts.) This is where the other part of the book is particularly useful.  

In the developing world, banking tends to be restricted to affluent clients. . FinTech does not challenge major incumbents; rather, it represents more of a development opportunity. FinTech allows for servicing relatively small transactions (by Western standards) which is compensated through a relatively large number of transactions. In this way, financial inclusion becomes a business and stops being a form of charity.

Mr Skinner illustrates extensively how far and deep these trends are going. In sub-Saharan Africa, mobile banking and e-wallets lead with the overall number of accounts growing fivefold between 2011 and 2016, reaching around 275 million accounts out of 420 million mobile subscribers. Interestingly, use is not evenly spread. Institutional design continues to matter even in the age of FinTech. In some countries, these developments are led by mobile network operators and in others by banks. Some countries actively encourage partnership and agreements to enable domestic and cross-border money transfers cheaply.

This is not just a matter of convenience. If you cannot get paid reliably and must rely on cash, there is a limited number of business opportunities that can thrive. The case study of China’s Ant Financial is therefore fascinating. It starts with a problem of trust between buyers and sellers that limits the development of the e-commerce that evolved into what we call now electronic payments. One of the lessons of this is really about the central role that the consumer plays. The business scale is staggering: in 2016, the value of transactions in the peak day (called Singles’ Day) was double the amount transacted on the US’s Thanksgiving Day, Black Friday and Cyber Monday together. It’s not just payments, as there seems to be an emerging pattern that starts with electronic payments and moves to managing money, and Ant’s money market fund is already larger than JP Morgan’s US Government money market fund.

And what about society? Living longer, 3D printing, the Internet of Things and conquering space may well change how we live. I am sure you have heard before the old dictum that this time is different. Perhaps this time it is indeed, if only for banking because of FinTech.

Monitoring the Risk and Business Impact of AI-Based Solutions

AI-based solutions can shape how financial services businesses make money, whether the business model is the same or not. For an existing financial services business, the motivations may vary and range from efficiency to expanding the business. There would be project risk as with any development, but leaving that important consideration aside, it is worth bearing in mind that AI-based solutions would also impact the risk profile of the business. This may or may not be the original intention, but it becomes more likely. The key implication is that implementing an AI-based solution would require a radically different risk oversight approach by the business.

Standard computer algorithms which are not AI-based can—and do—solve complex problems. The main feature of such algorithms is that the problem is somehow defined and an algorithm developed to solve it which will produce the same answer as long as the same inputs are provided. So a credit-scoring mechanism calibrated to capture a certain type of client gives you just that.

The answers offered by an AI-based system may change over time. New data is used to reassess the underlying relationships and recalibrate the relationship between the target variable and the potential explanatory variables. This “learning” can also happen in a standard programme when there is a process of recalibration. The difference is that in the case of AI, learning would happen on a real-time basis—that’s the essence of AI.

Alternatively, with AI a target variable may not have been defined. That’s not as unusual as it might sound. For example, algorithms assessing a loan or credit card underwriting may fall in this category because there is no single rule to predict a borrower’s likelihood of repayment. New data can lead to a certain recalibration or can be used to identify new relationships between certain data. For example, over time an AI-based system might identify that outstanding debt is a better predictor of the likelihood of borrower repayment than repayment history and penalise someone with a relatively good track record of timely repayments.

The first type of AI-based solution is called “supervised machine learning” and the second one “un-supervised machine learning”. The key difference is the extent of autonomy that goes with the learning.

Consider the potential impact on conduct risk of AI-based tools. One of the expectations from Treating Customers Fairly (TCF) with respect to product governance is that they are designed to meet the needs of identified consumer groups and are targeted accordingly. This requires a clear business strategy, including identification of the target market through a combination of qualitative and quantitative research and oversight of the business to ensure that it is aligned with initial expectations of customers and business generated. Take the example of automated investment services covered in a recent FCA review. These providers would rely on some type of AI-based solution, whether supervised or unsupervised machine learning. The possibility of capturing different customers or the advice generated being different from what was envisaged cannot be ruled out. The challenge is how to put in place a monitoring approach which ensures that outcomes and risks which arise are consistent with the expectations in the business plan.

Something similar can apply from the perspective of credit risk, impacting the quality of the portfolio and performance. Suppose you have been targeting retail customers with a specific risk rating for a credit card business. If you roll out an AI-based solution to enhance the efficiency of product underwriting, you would need to have in place mechanisms to ensure that the credit quality of the portfolio is consistent with your expectations—or else change those expectations. Both options are fine. You may want to keep your target credit rating constant and seek more volume, or perhaps you see AI-based solutions as a more robust tool to support decision making and, in a controlled manner, can relax your target rating. Regardless of your choice, you would need to put in place a credit risk monitoring approach that is suited to the new AI-based solutions, as well as ensure that the business understands the portfolio implications of “learning” that is at the core of an AI-based solution system.

The salient point to take away is that the roll-out plan of AI-based tools may focus on the launch. However, the greatest challenge may well be the need to provide for the ongoing and timely monitoring of the AI-based tools and their integration in business governance and risk management, which I will cover in the next post.

Artificial Intelligence (AI) – Fear or Uncertainty?

During the Q&A session at a recent panel discussion on FinTech, some people in the audience spoke openly about the fear of AI. This appeared to be driven by the potential impact of AI on jobs. Technological progress has often led to changes in the labour market, destroying and also creating new jobs. Who knew a web designer 25 years ago?

A lesson I take from world events and other experiences is that dismissing people’s fears about AI (or anything else, for that matter) is not usually an effective strategy. Unfortunately, I do not have a crystal ball to predict the future or provide reassurance. Instead, I thought I would take the hard road and write something about the genuine uncertainty that exists about the progress of AI and its potential value.

It is useful to start by taking a step back and thinking about the key aspects of AI tools. The main purpose of these AI tools is to make predictions about the future based on historical data and their underlying correlations. If you have been trained in economics, building models that employ data to generate predictions that support decision making is nothing new. 

AI, however, is different because advances in computer technology facilitate real-time predictions based on an extended range of data sources, including data originating from social media, text and many devices. Not surprisingly, these enhanced capabilities can uncover dependencies and correlations that might not have been visible to the naked eye or with more traditional research methods or data sources. It is worth noting at this point that despite its progress, AI shares with more traditional research methods the challenge of distinguishing between correlation and causation.

Progress in computing brings with it the ability to build decision-making tools that transform predictions into actions that can be executed without human intervention. The simplest and most ubiquitous example is predicting the correct spelling as you type. An example in FS would be any AI tool that forecasts an individual’s or corporate creditworthiness, then offers a product with no human intervention. However, predictions will remain predictions.

Another source of uncertainty about the impact of AI is the breadth of these tools. So far, AI tools have been confined to what is usually described as “narrow” activities, such as the two examples above. The possibility of material employment substitution comes with what is called general artificial intelligence that operates in broader settings. There has been progress moving from these narrow settings, one example being Google’s AI tool which beat the world champion of the board game, Go. However, a breakthrough in which that gives rise to general artificial intelligence still remains undiscovered. There are different views about how close we may be to reaching that point, but the reality is that no one knows. In the end, does it really matter if the world will be very different in 100 years?

At the same time, it is worth remembering that in an increasingly complex and interdependent world, enhanced predictions can benefit everyone, for example by preventing diseases and helping people enjoy work more. There are countless stories of this. The latest one I have come across (The Economist, “Diligence Disrupted”, 12 July 2018) relates to the challenge of legally reviewing a huge number of documents as part of due diligence or preparation for court. This is a labour-intensive activity that usually represents a significant cost hurdle. AI tools (and there are many) play a triage role, scanning the many documents searching for specific clauses, specific aspects of clauses, or their absence. These then allow lawyers to conduct a focused review and legal diligence, resulting in greater job satisfaction and, potentially, cost savings for the client. As the article notes, the net impact on employment remains unclear. However, lower fees could lead to higher demand requiring more lawyers.

So there is uncertainty about the impact of AI tools and a fair amount of benefit to society that can be derived before that uncertainty is resolved. This does not mean that progress with AI will always be smooth; like any other tool, AI tools can be misused intentionally or unintentionally. This is where applying governance and risk management to AI tools would generate value, but I will leave that discussion for another post.

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Why should we consider artificial intelligence (AI) from a risk management perspective?

I have been reading about AI in financial services and thinking about it from a risk management perspective. What is there to be gained from this? 

AI, like computers and other innovations, is a general purpose technology. One of the insights from Tim Harford , economist and journalist, about the impact of this type of innovation is that sometimes it takes time for innovations to have an impact because people do not immediately change the mindset associated with the previous technology. The main example he provides of this time lag is electricity. Following his invention of the light bulb in the late 1870s, Edison built electric power stations in 1881 in the US, and within a year electricity was available as a commodity. Yet as late as 1910, when electric motors had more to offer, manufacturing still relied on steam power. You can read about this here.

I found Tim’s explanation for this conundrum compelling. Steam-powered factories were arranged in a specific form to benefit from the single steam engine through a central drive shaft that ran through the factory.  Initially, owners changed the source of power to electricity but to fully benefit from it, factories had to be rearranged according to a different logic. In addition, rearranging factories gave workers more autonomy and flexibility, and the way staff was recruited, trained and paid had to change as well. As a result, adopting electricity meant much more than simply substituting one source of power for another and the pace of adopting electricity was slow.

I think this analogy might be relevant in applying AI to financial services. AI offers a new way of powering decision making in businesses. The example of replacing steam power with electricity suggests that to get the full value of AI, financial services need to think about AI as more than enhancing or substituting for existing tools. Risk management requires a broader perspective to support decision making and achieve business objectives.  I would hope that considering AI from this perspective would help financial services business to fully benefit from AI. 

You can also help by submitting questions about AI from the perspective of risk management and governance. Send your questions by email (isaacalfonblog@gmail.com) or leave a comment.  I am not sure I will have answers, but who knows?

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Artificial Intelligence (AI) and the Board Risk Committee

The purpose of risk management in financial services is usually defined as to ‘protect and enable’.  The ‘protect’ dimension can refer to the franchise value of the business but is mainly about protecting from regulatory intervention. ‘Enable’ has a perspective of value (however defined) and achievement of company objectives. (Click here to read more about ‘protect and enable’.)

AI-based solutions, leveraging on vast amounts of data, are already a reality in the world of financial services, and these solutions are only likely to become more prevalent in the next ten years. What are the implications of AI developments for a Board Risk Committee? 

The simple ‘protect and enable’ approach suggests a number of points for discussion:

• How would your company evidence that AI systems comply with relevant legislation, e.g. non-discriminatory laws?

• How would the wider data needs of AI system cope with data protection legislation? What about the so-called ‘right of explanation’? What would be the impact of these wider data needs on cyber-security?

• What is the business purpose of introducing an AI system? Does the business seek to enhance operational efficiencies? Does it aim to enhance business performance? How would you ensure that this purpose is achieved?  

• What would be the operational impact of the deployment of specific AI tools in the business? Would it also alter the overall risk profile of the business? The profile of certain risks?

• What are the implications for risk governance, the risk management function and other oversight functions?

These are not simple questions that can be covered in a meeting of the Risk Committee. In some cases, the answer to the questions may not be clear-cut.  For example, an AI-based underwriting system can be deployed to enhance business performance or to seek operational efficiencies. In other cases, addressing some of the issues would require the development of appropriate monitoring systems rather than a point-in-time consideration.

However, it is also worth bearing in mind that unless you operate in a start-up business, there would be a fair amount of technology available which would not necessarily be based on AI, and can be applied to improve existing business processes and reflect a (more) customer-centric perspective.  So perhaps the main question about AI systems is really whether there is an adequate understanding of technology in the business to ensure that AI is the appropriate technology.

So where should a Risk Committee start?  It may be useful to think about this as discussions outside the usual calendar of the Risk Committee meetings and develop a programme that consider these over time.

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Taking Risks: Lessons from a Politician

In my spare time, I like to read about current affairs. I have an interest in Brexit and its resulting economic impact which I covered well before the referendum here.  My current reading list is here.  My interests include the Middle East, and it was with that in mind that I picked up a book by the late Shimon Peres, former President of the State of Israel, which he completed just before he passed away in September 2016.  He also served as Finance Minister when hyperinflation was one of the main features of the economy and initiated a bold programme that tamed inflation successfully.

I found the title of the book, No Room for Small Dreams, a bit puzzling. I guess I did not expect a book title that reflects on someone’s achievements to start with ‘no’.  In any case, the book was quite interesting, articulating Peres’s role in some of the policy challenges of the State of Israel.  However, I can never stray too far from my professional interests, and I found that the book included a good many observations relevant to the practice of risk management.

The first observation is that often, not taking a risk is a risk in itself.

So many times in our lives, we struggle to confidently leap forward, averse to the possibility that we will fall flat. Yet this fear of taking risks can be the greatest risk of all.

People in risk and compliance functions should bear this in mind when they advise against a course of action.  However, if you want to take risks or are implementing regulatory risk requirements, you will need to consider meaningful options:  

I’d come to believe that when you have two alternatives, the first thing you must do is look for a third—the one you did not think of, that doesn’t yet exist.

I learned about the virtue of imagination and the power of creative decision making. ... We were quick and creative, and boldly ambitious, and in that we found our reward.

The challenge is really about options being meaningful.  That is not straightforward and requires consistent support from leadership:

“We have to use our imagination and examine any idea, as crazy as it may seem,” I insisted to those assembled. “I want to hear the plans you have.”  “We have no plans,” responded one. “Then I want to hear the plans you don’t have,” I replied.

If leaders demand allegiance without encouraging creativity and outside inspiration, the odds of failure vastly increase. … [W]ithout emboldening people to envisage the unlikely, we increase risk rather than diminish it.

Interestingly, it is Peres’ view that leadership also has an obligation to understand the technical details of the subject matter. 

I felt it essential to gain a degree of mastery in the science that would be driving the project. In previous endeavours, I have come to understand that in addition to a clear vision and strategy, true leadership requires intricate knowledge—a facility with the granular details of every aspect of the mission.

And finally, a word of caution about learning too much from failures:

It is only after we see failure that we can know if we misjudged the risk. ... But one must avoid the temptation to overlearn specific tactical lessons born out of failure or success. … This is one of the hardest things for some leaders to understand: a decision can be right even if it leads to failure.  

This is something that I have covered here. It is not an easy perspective for politicians and business leaders, though I’d like to think that this is where governance might prove itself valuable.

  

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