Author Archives: Henry Scanlan

Life after LIBOR? Transitioning to alternative rates

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For decades, the LIBOR – or London Inter-Bank Offered Rate – has been practically the most important numerical figure in existence. It’s been used as the global interest rate benchmark by mortgage lenders, student loan officers and credit card agencies, among others, all of which set their own interest rates relative to what LIBOR dictates. 

Until now. 

From 2021, LIBOR will be scrapped, amid recent rigging scandals that resulted in billions of dollars in fines for major global banks. The financial industry will have to make friends with new ‘risk-free’ alternatives to LIBOR including SONIA (Sterling Overnight Index Average) or SOFR (Secured Overnight Financing Rate).

All of which means that in less than two years, banks must pull off the existentially daunting feat of transitioning all of their contracts to alternative interest rates.

Robotic process automation (RPA) and natural language processing (NLP) can do the “grunt work” of analysing documents and pulling out keywords, but when we get to the meaty part – i.e deciding what to do with each contract – the process becomes all about multi-factored decision-making, which is better suited to knowledge-first, rules-based automation. Identifying the best fall-back provision in the contract for situations when LIBOR is unavailable, or which are expiring before 2021 and can therefore be disregarded, or how much additional risk to price in – these are the types of judgements that typically require human oversight. They’re also dependent on thinking processes that can be encoded in rules-based technology. Tools that can make this thinking readily available – such as an automated triage tool linked to a centralised knowledge base – can vastly improve the rate and accuracy of contract issuance and revision, under post-LIBOR terms.

In a world of curveballs like Brexit, amending large numbers of contracts could become something of a recurring theme for businesses. For large banks, getting the armoury in place now will not only appease the FCA’s urge to have a “robust written plan” in advance of the change – it’ll save millions in the transition period, when that mountain of contracts beckons.

Transparency in a post-LIBOR world

Despite the short-term operational challenges, a post-LIBOR improvement of the market structure is a real possibility. Among many, many other things, what the financial crisis showed us was the vulnerability of IBORs to manipulation. To calculate LIBOR, each morning from the 1980s to the 2010s, the British Bankers’ Association (BBA) collected interbank-offered rate quotes from a panel of banks, reflecting the rates at which banks said they could borrow funds from other banks. These were hypothetical numbers that didn’t necessarily have to match up to reality, leaving obvious room for foul play. The transition to rates grounded in actual transactions and liquid markets, rather than derived from speculative polls of selected banks (as IBORs were), should be viewed as an ethical step forward.

But in the midst of an FCA crackdown, firms will have to do more to execute this new trend toward total transparency. Each trade or loan will need to be backed up by comprehensive contextual information about each trade, including how a certain price was reached and how a firm measured the liquidity profile of a trade, right down to intricate details such as how long an employee has been trading for.

Failure to invest in transparent technology to properly audit trades could see firms and traders incurring the same regulatory retribution and unhealthy headlines as ex-UBS trader Arif Hussein, banned by the FCA for attempting to manipulate sterling rates. 

Opportunities of risk-free rates

The various aforementioned ‘risk-free’ rates, including SONIA and SOFR, are currently having varying degrees of takeup and liquidity. But the likelihood is that firms and traders will soon have to fully commit to a world in which there are multiple rates to choose from to best serve specific segments of the market. This could mean a wealth of new opportunities to find the perfect match of rate with product – if firms can identify them. Again, this sounds like a large-scale task but it would be a breeze for knowledge-first automation to aid in the assessment process of defining risks, deadlines and term structures, per rate and product.

This touches on another major element of the transition – education and knowledge sharing. In insurance, for example, the widespread use of new benchmarks will require the education of loan officers, staff and customers. Transparent automation technology that can make information on each rate widely accessible will go some way to achieving this.

The end of LIBOR is going to be a perfect storm for the financial services industry – one of those tide-turning moments that has the potential to leave everyone adrift and lost at sea. But organisations should be implementing transparent intelligent automation now to ensure they reach safe harbour. 

For more on how businesses can navigate legislation change with technology, download our eBook below. 

Download the eBook: Overcoming financial regulatory bloat
Learn how to keep financial expertise in your organisation (even if the experts are leaving), so you can automate and scale compliance operations.

Compliance bloat in finance - how to ensure your business stays afloat

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There was once a time – a simpler time, perhaps – when managers could run a skeletal operation of compliance workers taking on a variety of legislations. Nowadays, mammoth projects such as the implementation of Mifid II and the Brexit fallout require the creation of larger teams of specialists. 

For financial services firms, there are hardly enough specialists in the world to deal with such cataclysmic shifts – which is why demand is outstripping supply in these areas, leading to disproportionate wage growth among compliance professionals. For companies resettling on the European mainland post-Brexit, getting the right compliance staff will be a pain point. “You cannot just relocate people from London, you have to replicate them,” PwC partner Amanda Rowland told the FT recently.

Ominous commentary floods the business news, with dark clouds being cast by Brexit and IR35 reforms. Brain drain is forecast, and for businesses looking to strengthen compliance in the face of oncoming regulatory change, the headcount boom of the past decade is an unsustainable way of dealing with the problem. 

Regulatory changes and the compliance boom

For banks and financial services firms, there has been an alarmingly rapid rise in the compliance and risk management agenda over the past decade to cope with an increasing abundance of regulation. HSBC’s chief compliance officer, Colin Bell, recently stated, “You have to build an industrial-scale operation just to digest all the regulatory changes.”

This is far from exclusive to banking. Whether it’s ISFR 17 in the insurance sector, IR35 in the accountancy space, the transition away from LIBOR, or any number of logistical challenges associated with Brexit, businesses are having to scramble for the right tools to react to the impact of legislation.

Compliance and fincrime personnel now equate to 3% of a bank’s headcount, a figure that’s doubled in six years, according to Boston Consulting Group. But throwing money and resources at the problem, as banks have been doing, is not a sustainable way to continue.

Scale knowledge, not resources

There is a scalable and more efficient way for businesses to ease the load when it comes to matters of risk or compliance assessment. With just over half of Britain’s businesses believing the country is at risk of a brain drain after Brexit, capturing and maximising the expertise of specialists should be a priority.

The intricacies of legislation, and the often fine margins between complying or overstepping boundaries, require the nuance of an expert. These are decisions that contain an ounce of subjectivity; one assessor’s green light may be another’s red light. To be able to make judgements at scale, and consistently in line with a company’s recognised best-practice, then technology needs to be devised with human subject matter expertise at the heart of it all. 

The looming brain drain, as a result of the regulatory turbulence of Brexit and IR35, will mean financial companies can no longer rely on increasing manpower to improve compliance. Download the eBook below, in which we explore ways around an upcoming talent exodus from financial institutions. 

Download the eBook: Overcoming financial regulatory bloat
Learn how to keep financial expertise in your organisation (even if the experts are leaving), so you can automate and scale compliance operations.

Don’t be a statistic: the human element that separates machine intelligence from machine learning

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Last year, Apple launched a credit card. Not long afterwards, the card was exposed – by Apple co-founder Steve Wozniack himself, no less – for offering disproportionately low lending rates to women. A company that includes “Accessibility” and “Inclusion & Diversity” under its list of ‘Five Core Values’ had unwittingly created a tool for gender discrimination. Female customers had been reduced to unfavourable statistics by a biased algorithm.  

“The road to hell is paved with good intentions.” 

Crucially, Bloomberg Businessweek, reporting on Goldman Sachs’ administering of the Apple Card, wrote that the bank did not “intentionally” discriminate against women, but that this “may be the point: the complex models that guide its lending decisions may inadvertently produce results that disadvantage certain groups.” 

The fact that this disicrimination was unintended, and caused by a product marketed by Apple on the values of “simplicity and transparency”, is evidence that we have a serious problem. The machine-learnt technology driving so much of today’s algorithmic decision-making often blindly derives results from statistics in ways that humans can’t understand, meaning that users, regulators and consumers are blindsided when things go wrong. Better the devil you know, as the saying goes. 

Our understanding of AI is outdated 

Before we become dictated by the laws of unintended consequences, the time has come for us to remodel our understanding of AI. This decade, let’s separate the old from the new: Machine Learning (ML) from Machine Intelligence (MI). 

With the hype-cycle of ‘Tech Twitter’ bundling countless technologies together under a single #AI hashtag, it’s become easy to categorise everything AI-related into the branch of ‘the new’.

But it’s more pragmatic and sensible to accept that some of the technologies beneath the ‘AI umbrella’ have been around for years, and some are doing little to accelerate the field. 

In many cases, ML is actually taking us backwards, to a time when such practises as discriminatory credit scores and mortgage “redlining” were the norm. 

Part and parcel of our problem is that we’re viewing AI through a narrow-minded lens, with a focus on statistical methods.

As technology activist Cory Doctorow pointed out recently, the endlessly retargeting, predictive nature of machine learning, which places the utmost priority on legacy data, is fundamentally conservative and status-quo-driven.

Of course, this alone doesn’t mean we should avoid using it. After all, the wheel still works perfectly, which is why it doesn’t need reinventing. But there are anachronistic elements of ML that are ethically problematic and dangerous. By relying solely on statistical analysis of data to drive decision-making, we open ourselves up to unintended biases in those data, and society pays the price with the surrender of its values of fairness and equality. 

The problem with relying on statistical methods

Multidimensional, multivariate numerical analyses on big data are largely impenetrable to humans and over reliant on data. We don’t readily know the features in a data set these models are selecting on when automating a decision, and have no evidence for why the algorithm may suggest a particular solution.

True, machine learnt models are sometimes able to provide interpretable ‘explanations’ of what features in the data they are selecting on. But they will always require human judgement to be able to interpret this and describe why.

Automating decisions that will affect customers without being able to communicate how and why those decisions were reached is unthinkable. Yet with many data-driven, statistical approaches this is precisely the situation companies continue to find themselves in, despite 70 percent of global consumers valuing transparency about data usage as the key to trusting a business.

Machine intelligence grounds our technology in human knowledge

Machine intelligence is a new leap. If we are really to enter a future of AI-driven growth that society can embrace, we must adopt technology that takes the best from both human decision-making and data, and grounds AI in human knowledge.

This is machine intelligence’s modus operandi. Machine Learning blindly derives from numbers, emotionless rules about objects in our world. Machine Intelligence derives an understanding of the relationships between objects in our world, based on the wisdom of a human modeller. Propelled by machine intelligence built by humans, for humans, these models blow the ‘black box’ wide open and make AI understandable.

By representing knowledge using reasoning principles rooted in probability and mathematics, machine intelligence enables businesses to synthesise and replicate the way their smartest people use their logic and expertise to make decisions – without requiring elite and expensive data science skills.

In the plainest of terms: this is how your people can take what they know, connect it to data, and use it to solve large-scale problems.

How your smartest people can encode their own logic to solve high-value problems

Rather than simply apply potentially misleading (or illicit) historical data to new scenarios, machine intelligence enables businesses to apply complex human logic to each case, at an unprecedented scale. 

This may not be a case of “out with the old, in with the new”: there is a place for both ML and machine intelligence to work in tandem, as we will explore in my next blog post. But without the human intelligence part, we are simply replacing ourselves with cold, statistical machines, losing the ability to explain our decisions, and repeating all of our old mistakes. 

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

What will the CEO of the future look like?

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What does the workforce look for and value in a leader in 2020? With massive change taking place in the global economy, what will the CEO of the future look like?

Technology is now becoming so integral to all businesses that those with IT skills will need to be part of boardroom decision-making. “Looking to the future, I predict significant growth in the role that ‘techies’ will occupy in their businesses,” says Ben Taylor, CTO of Rainbird.

Read the full article, ‘What will the CEO of the future look like?‘ via Relocate Global.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

The importance of a transparent approach towards AI technology

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We’ve seen increased interest and concern around government and private sector use of AI technologies. Numerous media stories have highlighted contentious applications of AI, including the implementation of a visa application system by the Home Office and a facial recognition system by the developers of the King’s Cross Central precinct in London.

Many AI technologies are still in a nascent stage, which is part of the issue, resulting in flawed decisions. As the march towards an AI future continues, governments and public sector organisations need to consider and agree what applications are ethical, useful and acceptable in a democratic society.

Read the full article, ‘The importance of a transparent approach towards AI technology‘ via Open Access Government.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Automated inter-dealer brokerage

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AiX approached Rainbird with an aim to transform the Inter Dealer Brokerage market by making trading simpler, more efficient, and more transparent.

The project brings progress to a market in which the big three firms have acquired their competitors rather than innovating to gain competitive advantage, which has led to outdated practices that are both expensive and unfit for purpose.

The big three trading firms have acquired their competitors rather than innovating to gain competitive advantage, which has led to outdated practices that are both expensive and unfit for purpose

The problem
Inter-Dealer Brokerages (IDBs) allow large-volume traders to transact, but the billion-dollar market is outdated:

•      the services are notoriously slow, unreliable and expensive.
•      calculating and executing multiple deals requires substantial time and manpower.
•      new exchanges, tokens, cryptocurrencies, and platforms all need to be incorporated into the way deals are made.

In response to these challenges, AiX approached Rainbird to create a virtual broker that would increase productivity and transparency, and in doing so eliminate the errors, abuse and criminality that plagues financial markets that rely on human brokers.

The solution
The Rainbird-powered solution is an IDB virtual assistant, which offers traders the same quality of experience as a human broker would, but with much higher productivity and better outcomes.

To achieve this, the Rainbird team sat down with AiX to capture their trading expertise, building a Rainbird knowledge map with an understanding of concepts such as volatility and liquidity and their influence on pricing. Using this knowledge, Rainbird is able to review trade offerings and check them against the market to ensure that users do not waste time with unrealistic offers. Similarly, Rainbird checks responses from other traders to ensure their viability.

To compare trade offerings, Rainbird makes an API call to external data sources to retrieve the necessary information, and applies its trading concepts to calculate which recommendation to provide to the trader via an intelligent chatbot.

The outcome
When securing deals using AiX:

•      the response time for trading clients is more rapid, delivering more efficient trade confirmations.
•      the Rainbird chatbot engages in multiple marketplace conversations simultaneously in a way that a human broker never could, enabling trading to operate at enterprise scale.
•      brokers are freed up to focus on client relationships, while still maintaining close control over the Rainbird-powered automated trading.
•      the errors, abuse and criminality that plagues financial markets that rely on human brokers are eliminated.

Rainbird’s ability to explain its own actions, and provide an audit trail, is particularly important to the AiX project, as automated decisions in a closely regulated industry such as stock trading must be transparent.

Rainbird has signed an agreement for an exclusive license with AIX. In a market in which the top three players have annual revenues in excess of $6.5bn, this new tool is expected to attract approximately 75% of IDB revenues, while reducing commission by more than 50%.

AI-brokered trading with AiX is now underway – a world-first for the trading market.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Motor Liability Decisioning

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AIS provide cognitive technology solutions to the UK motor insurance industry and are currently focused on helping insurers and third-party representatives make better accident liability decisions.

The problem

Identifying who was at fault in a motor accident (referred to as a liability decision) is a difficult task and an historical area of weakness in the motor insurance industry. There are a number of problems with the liability process which increase claims costs throughout the industry.

The cost of car insurance now gobbles up 10% of a young driver’s average salary. Correcting issues at the beginning of the claims process can reduce the costs of slow or incorrect liability decisions.

First Notification of Loss (FNOL) is the process where a customer first notifies their insurer. These calls are generally handled by contact centres where employee turnover is high. Contact centre roles are often low paid, employee engagement poor and handler error is common as a result.

A poor liability judgement at FNOL can cause claim delays and result in significantly higher costs being incurred by the insurer later in the claim process. Poor liability judgements can also impact an insurer’s reputation and customer satisfaction levels.

An incorrect liability decision may be costly, but even a slow liability judgement at the outset can affect the insurers ability to retain control of the claim.

Insurance fraud cases are expected to double with the rise of automation. Better liability decisions can spot fraudulent claims earlier in the process

While liability has been a significant area of friction in the industry for some time, Rainbird is the first technology recognised as capable of truly automating this process, providing a model of liability that is capable of delivering comprehensive complex judgements.

The solution

The Rainbird team sat down with the AIS liability experts to encode their best-practise methods into a knowledge map. Rainbird takes each claim’s data and compares it with the knowledge map, asking efficient questions via a simple chat interface when it needs more data. The result is an accurate automated judgement, using a probabilistic method modelled on human expertise.

“I don’t come from a development background at all – I’ve got no professional IT skills – but I’ve been able to use the platform to code the knowledge that we’ve got.” – Danny Thomas, Chief Solutions Architect at Automated Insurance Solutions

Rainbird handles any uncertainty and missing data and simply takes that into account when providing feedback.

“Existing liability solutions actually require a great deal of understanding from the users in order to get the best from them.” – Ben Taylor, CEO of Automated Insurance Solutions

Each outcome includes a rationale and a full audit trail backed by evidence, satisfying the client’s obligations to Treating Customers Fairly (TCF) and their other regulatory obligations.

By implementing the solution within a liability process, insurers and others in the motor claims industry see a material improvement in both the accuracy and consistency of front-end liability decision-making.

Based on current industry claim costs, even a reduction of 2% will yield savings of more than £40 per claim.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Intelligent fraud detection

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Rainbird was approached by an international credit card provider who processes over half a million transactions per minute.



The number of reported fraud cases in the financial sector nearly doubled in the last year, with businesses struggling to keep up with ever-shifting vectors of attack, from new modes of mobile fraud to skyrocketing rates of identity fraud.

They wanted to improve fraud detection, reduce the time it takes to deal with false positives and improve customer satisfaction.

The ISMG reported that only 34% of C-level leaders have high confidence in their organisation’s ability to detect and prevent fraud.

The challenge
The complicated fraud-detection process required an offshore team to continuously monitor data feeds on multiple screens to determine whether customer transactions flagged by their algorithms were fraudulent or genuine. This manual process led to erroneous judgements, resulting in many unnecessarily blocked cards and huge frustration for customers.

40% of cardholders abandon cards after false declines, and a quarter of these people move their cards to the back of their wallets

In 20% of cases, the offshore team phoned the customer directly to request additional information, which resulted in expensive security steps, language barriers, and, in some cases, lost customers.

The solution
We spent time with the company’s fraud-prevention Subject Matter Experts (SMEs) to create a Rainbird model that identifies fraud by replicating their best-practice methods, without relying on humans. This reduced the time spent on each case, and improved accuracy and consistent decision-making.

To do this, Rainbird took the logic from over 50 fraud cases, along with the expertise of three of the business’ best-performing SMEs to model a Rainbird-powered fraud engine that represented the company’s best-practice procedures, which was capable of handling multiple concurrent transactions simultaneously and consistently.

We also created a new model for prioritising cases that needed expert involvement so that the most high-risk cases can be assessed by the client’s most experienced on-shore team members.

Rainbird automatically calculates every transaction with a percentage likelihood of being genuine or fraud.

The outcome
The result of our work was an 85% automation of all cases and a 60% reduction in back-office processing costs.

The new process avoids the less accurate and inconsistent judgements that were being made by the client’s offshore team. 

The company benefits from:

  • Increased detection rates
  • A reduction in the number of false positives
  • Cost and inconvenience of outbound phone calls to customers

Calls to customers are now backed by Rainbird’s in-built Audit trail, which can explain why Rainbird has concluded a fraud risk and better inform the customer conversation leading to consistent, better-quality calls, and demonstrably better customer outcomes.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Cognitive Tax Advice

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The problem
Highly skilled specialists carry out tax judgements on behalf of their clients. These assessments are incredibly complex and time consuming, not least because of the size of the entities involved. The complex nature of their work means it takes a long time for new staff to be fully effective.

The assessment process is manual and there are no existing tools that can combine financial assessment data with the human cognitive reasoning process undertaken by their tax specialists.

Like all firms, our client relies on some seasoned experts with tremendous experience. This focused expertise, combined with the long lead-time up-skilling new members of staff, was the driving force behind them selecting Rainbird.

Rainbird can make complex, human-like judgements based on a combination of real-world expertise, the applicable rules and regulations and client-specific data. What’s more it can do this at scale. Rainbird won a competitive process because of its ability to form judgements in the face of uncertainty and missing data, and because it can provide a comprehensive audit trail that can explain the rationale behind each and every judgement. This is a feature that set’s Rainbird apart from most other AI technologies.

The solution
Rainbird developed a proof of concept, delivering a tool that successfully augments back-office workers when making complex tax judgements. The project focused on a specific area of foreign tax legislation.

Our team worked with the client’s tax specialists to develop a Rainbird model that replicates how experts navigate the tax legislation. The tool can determine whether or not a specific company is within scope of the law, and identify the impact on their tax liability.

We also developed a roadmap for other solutions based on the concept of a central repository of knowledge, with the potential to power multiple applications in numerous parts of the business.

The outcome
Following a successful proof of concept, we are now working with the firm to build an internal Rainbird Centre of Excellence, enabling our client to rapidly build their own internal Rainbird projects. This build of a CoE will include working on real-world projects, delivering two new tools capable of augmenting the work of humans when advising clients on two new areas of tax legislation.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.

Bank Support Agent

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Rainbird was selected to work with the retail arm of a multinational banking and financial services company. The bank operates an extensive branch network and uses a contact centre to support their millions of customers in their banking needs.

The problem
The bank receives thousands of enquiries every day, many of which relate to regular and recurring payments such as direct debits and standing orders. Staff have access to vast amounts of information internally which they rely on to resolve these queries.

Like many such information stores, the content is very substantial and wide-ranging. Staff have to navigate these articles and on finding an article that might be relevant, must scan through pages of detailed information to find the section relevant to the specific query they are dealing with.

The articles contain general guidance, and the employee needs to determine how best to put this in context for each individual customer. This process can be time consuming, for both staff and customers.

Last year, employee turnover in banking was reported to be at a ten-year high. Banks can cover this loss of experience and know-how by encoding their expertise into a knowledge map.

Knowledge degradation is also an issue, in areas of the bank where staff turnover is higher, such as in contact centres. Expert knowledge can be lost if experienced staff leave, and the consistency of judgements can suffer as a result.

Less experienced staff tend to seek out experts, who then become a focal point for queries, detracting from their own day-to-day responsibilities.

The solution

The bank engaged Rainbird to build a tool that any employee could use to answer client questions and resolve problems with complex recurring payments. Rainbird was integrated with IBM Watson’s Natural Language Processing (NLP) front-end to deliver a chatbot that can resolve over one hundred different trajectories of enquiry. IBM Watson is used to identify the intention of the employee asking the question, and Rainbird then takes over, handling the staff consultation and decision-making process leading to a contextual solution.

The result of this collaboration was a Rainbird knowledge map incorporating all of their knowledge with the information from the internal knowledge management platform.

The resulting chatbot is available to staff who can efficiently consult with it to rapidly solve customer queries. The result includes a high-quality contextual recommendation including detailed advice on how to meet the customer’s needs and signposting to the actions required.

The chatbot is being deployed across the branch network and UK based contact centres, ensuring consistency of judgement across all areas of the business – using this powerful shared repository of knowledge.

Become a truly intelligent automation and decision-making organisation
Find out how Rainbird can ensure every decision in your organisation benefits from the required expertise.