Used by Delta Capita to publish ‘Points of View’ posts to the Publications section / the homepage of the main website.

The IBOR transition is both an opportunity and a threat to every financial institution. The opportunity is that a clean, well-managed IBOR transformation will enable a firm to take advantage of the possibilities that arise from changing one of the foundations of current financial markets. The threat is that a poorly managed, lagging transition will at best cost a firm market share and at worst incur significant regulatory attention and intervention.

The FCA as lead regulator is making it clear that Friday December 31, 2021 (YE 2021) is the hard deadline for the end of LIBOR. Industry bodies (ISDA etc.) and working groups are currently finalising the new fallback and trigger provisions for each product to create a transition path away from IBOR. The current proposals differ significantly across products, countries, and timeframes and the transition from unified IBOR term rates to multiple differing solutions is creating many issues, some of which have yet to be identified due to the uncertainty involved.

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In a recent article published in the Securities Lending Times SFTR 2019 Annual, Jonathan Adams (Managing Principal, Delta Capita) questions whether SFTR will ultimately deliver more efficiency and transparency to the securities finance market.

When the first Securities Financing Transactions Regulation regulatory technical standard (RTS) was released to market participants for consultation, it was met with dubious distain. Of the 153 fields, few could imagine how it would be possible to report more than 30 fields. Settlement matching had been on dates, security identifiers, counterparty information and economic terms.

It begs the question, how can a reporting regime of this complexity, fraught with the risk of matching failure, serve the regulator in determining the potential for systemic failure? Moreover, how can it benefit market participants?

Click here to read the full article.

James Proudman, Executive Director for UK Deposit Takers Supervision at the Bank of England, spoke on 4th June at the FCA Conference of Governance in Banking about the implications of artificial intelligence (AI) and machine learning (ML) on governance in banking.

Proudman told the audience that the governance of AI adoption is “a topic of increased concern to prudential regulators” since “governance failings are the root cause of almost all prudential failures” and that managing the associated risks is an increasingly important strategic issue for boards of financial services firms.

While Proudman made sure to highlight the potential benefits of AI applications in areas such as securities trading, anti-money laundering (AML), fraud detection and credit risk assessments, he stressed that as a prudential regulator, the Bank of England has a need to understand “how the application of AI and ML within financial services is evolving”, the implications on the risks to firms’ “safety and soundness”, and in turn, how these risks can be mitigated through the banks’ internal governance, systems, and controls.

Speaking in reference to a survey of AI/ML adoption in finance currently being conducted by the Bank of England and the FCA, he stated that there is general agreement that although AI and ML can reduce risks, “some firms acknowledged that, incorrectly used, AI and ML techniques could give rise to new, complex risk types”.

Proudman suggested that the retrieval, processing, and use of data may pose a significant challenge, pointing to three potential causes of data-related risk:

  • the expanding scale of managing problems related to poor data quality as data availability and sources balloon,
  • ethical, legal, conduct, and reputational issues associated with the use of personal data, and
  • distortions resulting from biases in historical data and assumptions built into ML algorithms.

His insistence on the “the need to understand carefully the assumptions built into underlying algorithms” and “the need for a strong focus on understanding and explaining the outcomes generated by AI/ML” sends a clear signal to firms to incorporate ML explainability tools into their model development and validation workflow. Directly applied on a ML model, such tools allow modellers and testers to understand both why any individual decision was taken and how the inputs to a model interact to make it work the way it does as a whole. ML explainability tools can also be applied after AI/ML is approved for use. As Proudman notes, governance has a role to play during the deployment and evaluation stages as well as for correcting erroneous machine behaviour. To ensure proper oversight, a ‘human in the loop’ can make use of explainability tools to support their decision in favour or against shutting down an algorithm, for example.

Proudman further proposed that regulations designed to deal with human shortcomings, such as “poorly aligned incentives, responsibilities and remuneration” or “short-termism” remain as relevant in an AI/ML-centric work environment and that it will be crucial to ensure clear individual accountability for machine-driven actions and decisions.

The implication that individual employees, including senior management, may be held responsible for actions or decisions taken by a machine reinforces the case for facilitating human-friendly model explainability. Boards should think about how the right tools best enable their workforce to comprehend the reasons for, say, a rejected mortgage application, and whether the model that made that decision did so because of built-in human biases. Since the person responsible will not necessarily be proficient in the language of AI/ML, it is crucial that these tools facilitate human-friendly interpretations and, in turn, informed decision-making.

Proudman also affirmed that he sees increased execution risks arising from the acceleration in the rate of AI/ML adoption and proposed that boards should ensure that firms possess the skill sets and controls to deal with these risks.

Boards should heed Proudman’s call to align their governance structures with the challenges of AI/ML. In addition to the obvious benefits to the business, having knowledge of what the models are doing and being able to explain how they work may prove invaluable when it comes to anticipating new rules for transparency and interpretability requirements of ML models.

Other related issues, such as data privacy, also have implications for corporate governance which can be addressed using AI/ML tools. As an example, sending human voice data to the cloud through voice-activated mobile applications may expose users to risks of illegitimate data use and can cause distrust in a firms’ data practices. To avoid this, model compression tools can be applied to reduce the size of speech recognition models and consequently allow voice data to be processed locally so that they never leave the device.

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Alexander Klemm

Consultant

Delta Capita

In this latest white paper, Edward Adcock & Thuy Nguyen (Data Science Consultants, Delta Capita) discuss machine learning model challenges, and introduces the Delta Capita DC MINT platform, which helps clients to address these challenges.

Click here to read the white paper in full.

Many articles have been written and discussed on the Three Lines of Defence model. Some have theorised on its implementation and many have collectively discussed the challenges that organisations have faced, and a few have outlined why it may not be appropriate. However, that all said, the FCA’s 2017 review of Compliance found that all firms that participated in the survey had adopted the Three Lines of Defence model.

In this article, David Long, Charanpal Matharu and Nick Wilcock outline some key insights observed by the Non-Financial Risk Practice at Delta Capita. This may prompt organisations to review the effectiveness of their framework.

Read more

Payment glitches that cause shopping chaos during peak periods like Black Friday have become an all too common experience for South African consumers. South Africans have bitten the Black Friday bug that happens every year in late November, and which is typically followed by Cyber Monday. The craze has only become bigger with every year.

During Black Friday 2017, Standard Bank’s transaction volumes on its credit and debit cards spiked more than 100 percent compared with the same event the previous year. In 2016, Absa said its total customer issuing spend for the Black Friday weekend was just more than R1 billion.

It’s unsurprising then that with high transaction volumes comes higher risk.

In this article, Earl McCausland (DC Managing Director, South Africa) & Trevor Belstead (DC Head of Transaction Banking Solutions) discuss bloated legacy systems, and why banks should ultimately consider utilising transaction monitoring technology, which allows them to instantly locate missing payments and proactively avoid payment outages.

Click here to read the full article.

The impact of the replacement of Interbank Offer Rates or IBORs is far reaching both geographically and by the sheer volume and diversity of products that use them as benchmarks (estimated 300 trillion USD equivalent).

In this article, Jonathan Adams (Delta Capita Managing Principal: Securities Finance & Collateral Management) writes that the operational impact to the banks, asset managers, insurers and their respective clients that issue, sell and manage IBOR benchmarked products will be significant.

Click here to read the full article.

Every day we generate 2.5 quintillion bytes of new data and it is accelerating. Approximately 90% of the total data we have today has been generated in the last two years. Harnessing the power hiding in this ocean of data has become an absolute strategic imperative for all businesses and governments.

In his recent article, Ricardo Cruz (Senior Consultant at Delta Capita) explains why machine learning is very much at the centre of this revolution, and how the last few years has seen an explosion of research and promising practical applications across a very broad spectrum of problems and industries… and Financial services are no exception.

Click here to read the full article.

An often overlooked critical competency of an organisation is its adaptability – the capacity of an organisation to effectively respond to new demands and circumstances, whether it’s customer-, regulator- or technology-driven.

Onno Bloemers (Delta Capita Insurance Lead) writes that while stability used to be everything in financial services and insurance, agility is nowadays an increasingly important quality.

Click here to learn more about a recent study benchmarking organisational change in Financial Services in the Netherlands, and some interesting findings drawn from the results.

Fintech Circle - Lack of Organisational Adaptability Threatens Digital Transformation

Don’t just copy other life insurers; learn from the best-in-class companies outside your own sector and apply these principles to your own business.

In the previous articles in the series, Onno Bloemers (Delta Capita Insurance Lead) discussed that customers do not buy insurance because they like it, but because they have to or there is no better alternative available. How you can turn a subject like insurance into something that customers actively engage in? Can, for instance, a pension become an urgent, relevant, integral part of our daily life?

The articles are based on a keynote presentation delivered at the Euro-Events Life Insurance & Pensions Conference in Amsterdam in Nov 2017.

Click here to read Part 3 (also click here to read Part 1 & here to read Part 2).