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REACHING THE NOT-SO DIGITAL NATIVES

DIGITAL

By Garry Hamilton, Group Business Development Director, Equator

 

It’s 2020. There’s no denying that banks and financial institutions have found themselves in a war against the tech giants in recent years. But can they win? Can consumers ever be truly satisfied? Or will institutions in this space stick with what they know regardless of how well it is working? In the digital-first now, FS companies have moved into an uneasy but rewarding landscape. Just as with consumer goods, they find themselves in a space where they no longer innovate ahead of consumer aspiration and demand, instead finding themselves increasingly under pressure to catch up.

 

The experiences consumers have with global giants such as Google, Amazon, Facebook and Apple (GAFA) define their expectations for all digital experiences. To stay up to speed, FS companies need to understand the shift in consumer demand as well as the multitude of threats to a business model that’s seen as traditional and staid. In short, they need to prepare.

 

DIGITAL

Garry Hamilton

The paltry, taped-together digital offerings from the incumbent financial service brands no longer stand. However, these brands still go to market with products and services defined by internal processes and limitations, giving little consideration to true service design principles or customer experience.

 

Thankfully this is changing, in part by credible (and incredible) upstart fintech companies, chipping away at the monoliths. At Equator, we work with several brands in this space, including Santander and Virgin Money, all of whom have realised the tides are changing. Major finance brands are no longer looking for the sharks in the water that come after all they do, instead realising that it’s a multitude of piranhas that pose the most significant threat.

 

Case in point: TransferWise has demonstrated that something as mundane as foreign exchange can be made fresh; Atom Bank has shown that a lean approach to savings and loans can drive solid business without being so reliant on rate, and Starling Bank has demonstrated that a serious focus on making the tech work can yield excellent results. Consumer choice for financial services has never been greater.

 

The hidden threats that GAFA may pose on traditional finance brands are, as yet, not fully realised. Apple has already demonstrated its ambition in the US with its digital credit card offering. Amazon has Amazon Pay and shown interest in the insurance market. Facebook is out there with its (stumbling) cryptocurrency effort, and Google’s feature-creep into aggregation (and payments) indicates a genuine and poorly understood threat from some of the wealthiest and most capitalised tech companies in the world. It’s hard to imagine a reality where consumers reject financial services from these brands.

 

But for incumbent brands in this space, the opportunity to maintain success lies in two key areas. Firstly, data. While it’s commonly understood that this is the currency that enriches the GAFA businesses, consumers’ financial behaviours are still broadly out of reach. Banks and financial institutions with historically loyal customers are sitting on a gold mine of data that can be turned into actionable insights. Insights that could deepen loyalty, increase relevance and make historically uninteresting and stuffy institutions appear modern and relevant.

 

Secondly, these organisations have significant human knowledge capital. These people know how the wheels turn, how to negotiate regulation and compliance, and how to manage risk. When you look to the most successful start-ups, their success is less borne of wealth, but more of knowledge and how financial systems operate. That cannot be underestimated. Banks and financial institutions need to strive to keep their staff loyal – not just the traders with their extreme bonuses. They’re not the ones that tech businesses would come after.

 

Getting a financial service off the ground isn’t cheap, but that’s not something GAFA worry about. Instead, it’s the complexity of negotiating the regulations and marketplace. What FS brands need to watch out for is that the fintech piranhas do not become sharks – not necessarily through growth but through acquisition and consolidation. Acquiring TransferWise, Monzo or Starling Bank is still pocket change to these organisations. And they DO have the technical wherewithal to bring autonomous platforms together and make a success of it, something high street banks and insurance companies have proven incapable to see through.

 

To survive and thrive, financial brands should take advantage of the one thing they’re historically good at – assessing and mitigating risk, with the critical difference being that keeping it the same as it’s always been is no longer the safe option. At Equator, we’ve already seen clients, such as AXA and Lloyds, acquire or partner with fintech start-ups. There’s a real effort from the high street banks to deliver a Monzo-esque functionality to their customer base. And we see real innovation in everything from insurance to loans and savings.

 

But there is still a long way to go. Regulation in the UK has been reasonably balanced between control and competition since 2007. However, technology continues to outpace the law, and we need to keep the pressure on the regulators to allow for new customer engagement models, new ownership models and new ways to deliver financial products and services.

 

In the last few years at Equator, we’ve assisted many major financial institutions take on tomorrow by helping them innovate and bring new products and services to life. We’ve helped Virgin Money bring their innovative B banking service to life, pioneered original service design in the most mundane of places for Tesco Bank and a lot more besides. We know that there are many enthusiastic brands out there looking to take on tomorrow and bring digitally-enabled services to life. But the sector still has some growing up to do. Crucially, it needs to accept that the disruption that came after the 2007 financial crash has nothing on what is around the corner

 

We’re still only really getting off the ground with the second payment services directive. Open banking is creeping in. We’ve yet to see the promised liberation of the payments sector (which should be huge), and it’s fair to say we should expect more niche disruptors to emerge, as money continues to pour into the sector. And that’s not even covering off the effect that machine learning and automation will continue to have in the industry over the coming years. If you ever dared to think finance was dull, get ready for a disruptive and exciting time.

 

Technology

WHY TECHNOLOGY IS KEY TO THE FUTURE OF AUDITING

By Piers Wilson, Head of Product Management at Huntsman Security

 

The Financial Reporting Council (FRC), which is responsible for corporate governance, reporting and auditing in the UK, has been consulting on the role of technology in audit processes. This highlights growing recognition for the fact that technology can assist audits, providing the ability to automate data gathering or assessment to increase quality, remove subjectivity and make the process more trustworthy and consistent. Both the Brydon review and the latest AQR thematic suggest a link between enhanced audit quality and the increasing use of technology. This goes beyond efficiency gains from process automation and relates, in part, to the larger volume of data and evidence which can be extracted from an audited entity and the sophistication of the tools available to interrogate it.

As one example, the PCAOB in the US has for a while advocated for the provision of audit evidence and reports to be timely (which implies computerisation and automation) to assure that risks are being managed, and for the extent of human interaction with evidence or source data to be reflected to ensure influence is minimised (the more that can be achieved programmatically and objectively the better).

However, technology may obscure the nature of analysis and decision making and create a barrier to fully transparent audits compared to more manual (yet labour intensive) processes. There is also a competition aspect between larger firms and smaller ones as regards access to technology:

Brydon raised concerns about the ability of challenger firms to keep pace with the Big Four firms in the deployment of innovative new technology.

The FRC consultation paper covers issues, and asks questions, in a number of areas. Examples include:

  • The use of AI and machine learning that collect or analyse evidence and due to the continual learning nature, their criteria for assessment may be difficult to establish or could change over time.
  • The data issues around greater access to networks and systems putting information at risk (e.g. under GDPR) or a reluctance for audited companies to allow audit firms to connect or install software/technologies into their live environments.
  • The nature of technology may mean it is harder for auditors to understand or establish the nature of data collection, analysis or decision making.
  • The ongoing need to train auditors on technologies that might be introduced, so they can utilise them in a way that generates trusted outputs.

Clearly these are real issues – for a process that aims to provide trustworthy, objective, transparent and repeatable outputs – any use of technology to speed up or improve the process must maintain these standards.

 

Audit technology solutions in cyber security

The cyber security realm has grown to quickly become a major area of risk and hence a focus for boards, technologists and auditors alike. The highly technical nature of threats and the adversarial nature of cybers attackers (who will actively try and find/exploit control failures) means that technology solutions that identify weaknesses and report on specific or overall vulnerabilities are becoming more entrenched in the assurance process within this discipline.

While the audit consultations and reports mentioned above cover the wider audit spectrum, similar challenges relate to cyber security as an inherently technology-focussed area of operation.

 

Benefits of speed

The gains from using technology to conduct data gathering, analysis and reporting are obvious – removing the need for human questionnaires, interviews, inspections and manual number crunching. Increasing the speed of the process has a number of benefits:

  • You can cover larger scopes or bigger samples (even avoid sampling all together)
  • You can conduct audit/assurance activities more often (weekly instead of annually)
  • You can scale your approach beyond one part of the business to encompass multiple business units or even third parties
  • You get answers more quickly – which for things that change continually (like patching status) means same day awareness rather than 3 weeks later

Benefits of flexibility

The ability to conduct audits across different sites or scopes, to specify different thresholds of risk for different domains, the ease of conducting audits at remote locations or on suppliers networks (especially during period of restricted travel) are ALL factors that can make technology a useful tool for the auditor.

 

Benefits of transparency

One part of the FRC’s perceived problem space is that of transparency, you can ask a human how they derived a result, and they can probably tell you, or at least show you the audit trail of correspondence, meeting notes or spreadsheet calculations. But can you do this with software or technology?

Certainly, the use of AI and machine learning makes this hard, the learning nature and often black box calculations are not easy to either understand, recalculate in a repeatable way or to document. The system learns, so is always changing, and hence the rationale that a decision might not always be the same.

In technologies that are geared towards delivering audit outcomes this is easier. First, if you collect and retain data, provide an easy interface to go from results to the underlying cases in the source data, it is possible to take a score/rating/risk and reveal the specifics of what led to it. Secondly, it is vital that the calculations are transparent, i.e. that the methods of calculating risks or the way results are scored is decipherable.

 

Benefits of consistency

This is one obvious gain from technology, the logic is pre-programmed in.  If you take two auditors and give them the same data sets or evidence case files they might draw different conclusions (possibly for valid reasons or due to them having different skill areas or experience), but the same algorithm operating on the same data will produce the same result every time.

Manual evidence gathering suffers a number of drawbacks – it relies on written notes, records of verbal conversations, email trails, spreadsheets, or questionnaire responses in different formats.  Retaining all this in a coherent way is difficult and going back through it even harder.

Using a consistent toolset and consistent data format means that if you need to go back to a data source from a particular network domain three months ago, you will have information that is readily available and readable.  And as stated above, if the source data and evidence is re-examined using a consistent solution, you will get the same calculations, decisions and results.

 

Benefits of systematically generated KPIs, cyber maturity measures and issues

The outputs of any audit process need to provide details of the issues found so that the specific or general cases of the failures can be investigated and resolved.  But for managers, operational teams and businesses, having a view of the KPIs for the security operations process is extremely useful.

Of course, following the “lines of defence” model, an internal or external “formal” audit might simply want the results and a level of trust in how they were calculated; however for operational management and ongoing continuous visibility, the need to derive performance statistics comes into its own.

It is worth noting that there are two dimensions to KPIs:   The assessment of the strength or configuration of a control or policy (how good is the control) and the extent or level of coverage (how widely is it enforced).

To give a view of the technical maturity of a defence you really need to combine these two factors together.  A weak control that is widely implemented or a strong control that provides only partial coverage are both causes for concern.

 

Benefits of separation of process stages

The final area where technology can help is in allowing the separation and distribution of the data gathering, analysis and reporting processes.  It is hard to take the data, evidence and meeting notes from someone else and analyse it. For one thing, is it trustworthy and reliable (in the case of third-party assurance questionnaires perhaps)? Then it is also hard to draw high-level conclusions about the analysis.

If technology allows the data gathering to be performed in a distributed way, say by local site administrators, third-party IT staff or non-expert users BUT in a trustworthy way, then the overhead of the audit process is much reduced. Instead of a team having to conduct multiple visits, interviews or data collection activities the toolset can be provided to the people nearest to the point of collection.

This allows the data analysis and interpretation to be performed centrally by the experts in a particular field or control area. So giving a non-expert user a way to collect and provide relevant and trustworthy audit evidence takes a large bite out of the resource overhead of conducting the audit, for both auditor and auditee.

It also means that a target organisation doesn’t have to manage the issue of allowing auditors to have access to networks, sites, data, accounts and systems to gather the audit evidence as this can be undertaken by existing administrators in the environment.

 

Making the right choice

Technology solutions in the audit process can clearly deliver benefits, however if they are too simplistic or aim to be too clever, they can simply move the problem of providing high levels of audit quality. A rapidly generated AI-based risk score is useful, but if it’s not possible to understand the calculation it is hard to either correct the control issues or trouble shoot the underlying process.

Where technology can assist the audit process, speed up data gathering and analysis, and streamline the generation of high- and low-level outputs it can be a boon.

Technology allows organisations to put trustworthy assurance into the hands of operations teams and managers, consultants and auditors alike to provide flexible, rapid and frequent views of control data and understanding of risk posture. If this can be done in a way that is cognisant of the risks and challenges as we have shown, then auditors and regulators such as the FRC can be satisfied.

 

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Business

BACK TO SCHOOL – CEOS NEED TO LEARN A NEW LANGUAGE, FAST!

By Simon Axon, Financial Services Industry Consulting practice lead in EMEA, Teradata

 

Chief Executive Officers of banks know all about change. Leading responses to new challenges, new opportunities, new regulation and new markets is all in a day’s work. But the existential challenge posed by Big Tech requires a totally new set of skills. It is an entirely different beast that inhabits a totally new environment and speaks its own language. CEOs now need to learn the language of data to survive in the emerging digital world.

Learning a new language later in life is hard. CEOs need to fully commit to accomplish it. Becoming data literate means mastering the basics of vocabulary and grammar. Gartner defines data literacy as the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.” Extending the language analogy: the building blocks are an understanding of logical data models – the basic vocabulary; meta data providing rules and information about data is the grammar.  Learning needs to go beyond parroting a few key phrases and acronyms. To really communicate in this new language CEOs must not only be data literate – but data cognitive. Language shapes thinking, and to succeed, today’s CEOs need to think data like digital natives.

Simon Axon

As anyone who has learned a language will recognise – practise makes perfect. This means rolling up your sleeves and getting into the data ‘lab’. Run some queries, experiment with data to test theories and learn how data can, and should, inform all aspects of business management. It is daunting, and different functions are fiercely protective of their data. But that’s one of the big cultural shifts the CEO needs to lead. Data is more valuable when it is used across the business. Developing safe and secure ways to combine, refine and analyse data at an enterprise level is fundamental to competing with Big Tech. The Chief Data Officer can help. Spend time with them and use them as a teaching-resource to get more familiar with what can and cannot be done with your data.

As you practise you will build confidence and move from school-level conversations to business-class data fluency. Spending more time looking at and working with data and you will begin to recognise ‘quality’ data, identify attributes and flag anomalies. This will build confidence and essential trust in data. Last year KPMG found just 35% of CEOs trusted the data in their organisations. This shocking stat undoubtedly stems from a data skills deficit among CEOs themselves. If they don’t know what to ask for, and can’t recognise what they get, they won’t trust it. To stretch our linguistic analogy, if you are not confident in the language then you’ll be anxious ordering food in a restaurant!

Ultimately, no one expects the CEO to personally implement data-analytics programmes across the business. But unless they have the confidence and the skills to accurately communicate what’s needed, to sit at the head of the table and ask the right questions about the menu, then the organisation is unlikely to put the right emphasis on the data strategy.

In How Google Works, former Google Chairman Eric Schmidt outlines how every meeting revolved around data – it is simply how Big Tech works. Banks need to adopt the same approach. Exploiting data in all scenarios must become second-nature. By modelling the use of data across the business – dissolving silos rather than sticking to narrow data sets that reinforce them, the CEO can define a powerful data culture. Operationalizing data strategy will, just like using language skills, stop data literacy from becoming rusty.

Entering any new market requires investment in understanding the language, culture and business environment. In the Big Tech world, data is the lingua franca informing every decision. Bank CEOs need to learn from them and invest in building their knowledge to become data fluent. There are no short cuts. Throwing money, bodies and tech at the problem will not get you there.

 

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