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FROM MANUAL TO MACHINE LEARNING: HOW TO APPROACH THE RECONCILIATION ‘PROBLEM’

By Christian Nentwich, CEO at Duco

 

At the start of 2020, before the global coronavirus pandemic changed the world, financial industry experts recognised that this would become the ‘decade of data’, with firms inundated with trillions of lines of data from a multitude of sources.

One of the many effects the current crisis has had is to amplify the need for resilient, connected systems and more robust processes. With business continuity front of mind, many organisations are looking for more efficient ways to manage huge swathes of data from multiple, disparate sources quickly and accurately. Data integrity is a key concern, and many are asking how they can automate their most critical processes.

However, despite the rush to digitalise many manual systems, automating reconciliations is still one of the toughest areas to crack.  Even pre-pandemic, automating this essential control function in financial services – which can help eliminate operational risk that can lead to fraud, fines, or in the worst case, the failure of a firm – was proving elusive for many organisations. Why?

Many organisations are facing a situation where there are a multitude of systems, different processes, technology types and computing.  Within that, there are three key reasons that make automation difficult:

  • A lack of standardisation – In many cases in financial services there are no strict data standards. For example, different counterparties provide trade and position data in different formats. Each one requires a bespoke reconciliation process or expensive data normalisation.
  • Increased complexity – Cash or stock assets can be matched on a few basic fields, but for more complex products you need to take far more information into account. Most current systems are unable to deal with every asset type that crops up in a timely manner. And, that’s before we get to the range of data needed for regulatory reporting, and the associated reconciliations required.
  • Poor data quality – The enemy of automation. Missing fields, inconsistent coding schemes and unavailability of common keys make automation difficult when using current solutions due to hardcoded assumptions within those systems.

However, in a world where the quantity and complexity of data that firms need to handle is set to increase exponentially, relying on manual systems and processes is no longer feasible. So, how do firms deal with this influx of data in the most intelligent way?

We recently launched ‘The Reconciliation Maturity Model’, a new roadmap that will help financial firms improve the automation, efficiency and integrity of data across all reconciliation and data matching tasks.  The model guides reconciliation practitioners through five key stages of reconciliation maturity, from ‘manual’ through to ‘automated’ and eventually ‘self-optimising’ – where machine-learning technology automates nearly the entire process, and where intersystem reconciliations are all but eliminated

Importantly, a more progressive approach to reconciliation automation will not only result in greater operational efficiency, it will also dramatically boost operational resilience, and put forward-thinking financial institutions in a better position to benefit from new technology and the added insight that intelligent systems bring.

The five stages of reconciliation maturity are:

  1. Manual – By this we mean using Excel or some other form of spreadsheet, macros, home-grown applications or – in some instances we’ve come across – printing out sheets of paper and marking inconsistencies with a highlighter pen! However, as the organisation grows, and the data becomes more complex, the risk of error skyrockets. There’s no audit trail, no governance and it becomes increasingly expensive to scale. If in the 2020s you’re throwing an increasing number of bodies at a data matching exercise, you know something’s wrong.
  2.  Hybrid – For the majority of organisations, this takes the form of a point solution, usually deployed to automate high volume, low complexity reconciliations such as cash or custody. These point solutions – by their very nature – tend to specialise in a certain type of reconciliation. Firms trading a wide range of assets, or those dealing with complex data, may need to use multiple point solutions to handle different reconciliation types. However, there will be many reconciliations that these point solutions are not able to handle elegantly. In these cases, firms tend to fall back on manual processes. The result is a patchwork quilt of different reconciliation approaches stitched together by manual work. The whole process is costly, difficult to keep track of, and difficult to scale.
  3.  Automated – All reconciliations are consolidated onto automated systems, and small teams build and onboard reconciliations, and oversee exception investigation.The key to getting to this stage is using the right technology. To reach Stage 3, firms need to be able to onboard reconciliations in hours or days, not weeks or months. They need to be able to rely on agile, flexible technology that can deal with complexity without multi-week data transformation projects. Once this technology is in place, complexity and risk can be vastly reduced, while increasing efficiency and transparency across processes.
  4. Improving – This enables greater efficiency and oversight of the reconciliation function as a whole. It also enables firms to normalise their data across the business and implement additional data quality checks across systems, highlighting areas of incomplete or incorrect data.  Organisations are then able to start consolidating systems and removing duplicate reconciliations which have already been handled upstream.  Processes become leaner, more efficient and more transparent.
  5. Self-optimising – Full automation is deployed across the entire lifecycle of reconciliation, from onboarding to exception resolution. There is very little involvement from staff and continuous improvement is possible via a machine-learning enhanced system. Internal reconciliations are removed, leading to major reduction in cost and complexity.

While stage five is the ‘holy grail’ that all financial organisations should be aspiring to, many firms are at the ‘hybrid’ stage, and making the leap to ‘automated’ is the most challenging step.  However, once at stage three, firms are more able to move up the process to ‘self-optimising’.  At this point, with enough training data, machine learning can spot errors, outliers and poor data quality at source, reducing the number of reconciliations required.

So, while we know that moving from manual to machine learning is not an overnight process, The Reconciliation Maturity Model provides a blueprint to getting there.

The Reconciliation Maturity Model is available for download here https://content.du.co/reconciliation-maturity-model-whitepaper

 

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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Business

REVITALISING THE TOKEN MARKET

By Gavin Smith, CEO at Panxora

 

With interest rates near zero and fears that whipsawing stock markets are set for further plunges, many investors are turning to alternative markets in the search for returns. Money flowing into cryptocurrency hedge funds and trusts like Grayscale is at all-time highs and the large cap coins seem to be entering a bull phase, but that capital is not trickling down into new token projects. Why are blockchain token projects struggling to attract funding?

 

Seed investor scepticism

Setting aside the reputational issues with mainstream investors, even those educated in blockchain tech are not signing on the dotted line. This is certainly due in part to the hangover from the early token market.

During the heady days of 2016/17, investors could buy tokens during the token sale, and if the project was legitimate – even if the business case wasn’t particularly strong – prices would soar based on market enthusiasm. Early investors purchased at a discount and cashed out almost immediately for a handsome profit – and then repeated the process again. The token sale allowed founders to amass a war chest large enough to finance the entire token project – without having to give up a large chunk of company equity. Everyone got what they needed out of the deal.

Running a token sale is far more expensive today than it was during the boom. Getting the attention of the token buying public in a market where advertorial has replaced editorial is expensive. This coupled with a regulatory framework that requires the advice of accountants, solicitors and information gathering of KYC details for investors all comes with an escalating price tag.

To accommodate the change in cost structure, tokens now need to acquire funding in two rounds. Frequently there is a first round where capital is raised from a few, large investors. This cash is then used to finance setup and marketing the main token sale. The token sale, in turn, provides the capital needed to run the entire business project.

 

Bridging the gap between token projects’ needs and early stage investors

To successfully get a token through the capital raising process, founders must acknowledge the risk assumed by those very early investors and reward them appropriately. And given that tokens may stagnate or fall in price post token sale means that a deep discount in token price is not necessarily attractive enough to get investors to commit.

Many tokens have turned to offering equity in the business in the effort to raise that first tranche of capital. If you look at the number of successfully concluded token sales, the downward trend has continued since Q2 2018, so offering equity is not sufficiently stimulating the market.

 

Two sides of the coin

So, what is the answer? It’s a complex question but one thing is certain. Any solution must be rooted in a deep understanding of what both parties need to successfully conclude the deal.

On the one hand, token founders’ needs are clear: they need enough capital to get the token ready for and through a successful liquidity event that will provide sufficient funds to build the project. The challenge lies in striking the right balance between accruing that capital and making sure not to offer so much project equity that give up either the control or the incentive founders need to drive the project forward.

On the other hand, while the needs of the seed capital investors are more complex, there are two areas of key concern: transparency and profit incentives.

 

Transparency can mean many things, but almost always includes providing more informative cost and profit projections, as well as answers to a whole range of questions, not least the following:

  • What happens to investor capital if the token sale event fails? Token founders must be transparent from the outset. The token market is highly speculative and early investors run the risk of losing their money should the project fail. Therefore, investors require a well-established fund governance process in place throughout the fundraising so they can make informed decisions on whether the project is worthwhile. 
  • How are the assets for the entire project managed? Investors need to know that their money is in good hands and that proper treasury management techniques are being used to manage cryptocurrency volatility risk. Ideally, an independent custodian will be used to hold the funds and limit founders’ ability to draw down the capital – releasing funds to an agreed-upon schedule of milestones.
  • How are the rights of investors protected, for instance in the case of a trade sale? Investors need to know what happens if the company they are investing in is sold. What impact could this have on the value of their stake? Would a separate governance framework need to be established? These are critical questions and investors aren’t likely to settle for any ambiguity in the answers.

Profit incentives are important when it comes to encouraging early participation in a project. Investors need convincing that the proposition will keep risks to a minimum and focus on providing a strong probability of a return. This means that founders need to be able to defend the case for the increase in the value of their token.

But this isn’t the only incentive that matters. Investors can also be incentivised by preferential offerings such as early access to projects and services that might help their own business.

Let’s not forget that investors don’t support just any project. What really matters is that there is something special and unique about the business being underwritten by the token. Preferably something that could be shared upfront and directly benefit the investor – proof that the investment is really worth it.

And that’s what it all comes down to. Ultimately, while token projects are having a hard time finding funds at the moment, if they can prove their worth and provide full transparency and clear profit incentives to ease investors’ concerns, the money is out there. And deals can be done.

 

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