Banking & Finance

Finance process automation - the key to unlocking potential

Finance Process Automation
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Financial processes are an integral part of an enterprise; their horizontal nature touches the entire organisation, they generate extensive amounts of data and carry the potential to identify performance issues, predict scenarios and change business outcomes.

Automation is key to unlocking such potential, but knowing exactly what to automate across your financial processes and what technologies are most suitable is key towards achieving impactful change.

The future of the finance function

Traditionally, enterprises delegated financial processes externally via outsourcing as a way to cost-effectively filter out high volume, low-value tasks. We are starting to witness automation’s role in allowing for such tasks to return in-house, and in turn, shake up the developing world’s information technology industry. World Bank estimates that 69% of today’s jobs in India are under threat because of automation, with China seeing a staggering 77% of jobs at risk.

According to Microsoft, organisations already using automation technologies at scale are performing an average of 11.5% better than those who are not – up 5% from the previous year (2018). Automation can propel transformation behind the scenes with a higher level of speed, scale and accuracy than outsourcing could ever offer (as outsourcing relies on large teams of specialists, prone to error due to language barriers or fatigue, completing manual tasks).  

Automation is a more effective option, allowing the ability to scale capabilities, generate higher quality output at greater speed, boost security and overall accuracy (which improves the customer experience). It also allows you to reserve higher-quality, value-focused tasks for your human workforce.

What exactly can be automated within your finance function depends on the nature of the task. McKinsey has illustrated (see below) that up to 42% of financial processes can be fully automated, with about a third of the opportunity for automation captured using task automation technologies such as robotic process automation (RPA). The remainder requires cognitive automation technologies, such as intelligent automation (IA).

Finance process automation

Image source: Bots, algorithms, and the future of the finance function, McKinsey 2018

RPA is best suited to automate high volume, repetitive, rules-based financial process tasks, such as general accounting operations that are governed by business logic and structured inputs. Intelligent automation can then sweep up more complex decisions that are out of reach for RPA. Intelligent automation is better suited to more high value, transactional decisions, that typically rely on a human to make a decision, such as the screening of R&D tax claim judgements or payment sanctions.

It can be common across departments, and even functions within departments, to operate using disparate technology systems. But this structure will inevitably lead to errors, due to data overlap or misinformation, or hinder digital transformation due to integration issues. Given the horizontal nature of financial processes (i.e. spanning multiple functions), alignment is imperative.

Utilising the compounding efficacy of a portfolio solution that combines RPA and intelligent automation, provides a truly end-to-end automation solution. One that seamlessly allows for knowledge sharing and operational alignment, across functions and departments.

Making end-to-end finance process automation a reality

A great working example of a truly end-to-end automation solution is delivered by Rainbird and RPA provider Blue Prism. This integrated tool adds a layer of complex and high-value decision-making to RPA’s typical process automation capabilities. Essentially, your “thinking” tasks can be automated in synchronicity with “doing” tasks.

Blue Prism bots normalise and move records from one place to another, while Rainbird’s intelligent automation decides what needs to be done about those records. Alternatively, Rainbird can make decisions about the implications of data, and then tell the Blue Prism bots how to tag or where to move the data.

Take the highly repetitive, rules-based process of payment sanctions screening. A bank system will generate an AML alert, Blue Prism will collect the alert and automate data gathering to classify the alert, while Rainbird will provide an automated assessment and return a percentage of certainty in its decision as to whether the alert is a ‘potential crime’ or a ‘false positive’. Rainbird will send this decision back to Blue Prism, which then actions the decision – if it is a ‘false positive’, the payment is automatically released, but if it is a ‘potential crime’, an alert is escalated to a human for further investigation. All this takes place within seconds and with a high level of accuracy and explainability.

End-to-end automation can service many different needs within the finance function, introducing unprecedented speed, accuracy and scale to high volume tasks.

Making financial data-driven decisions responsibly

Microsoft research found that half (51%) of financial services leaders say they do not know what to do if they disagree with an AI application’s course of action while nearly three in five (59%) admit they are unaware of how the technology reaches its conclusions.

This begs the question as to how a CFO could possibly know if the technology is doing what they need it to and how they can step in and correct things if it is not.

When choosing an end-to-end automation solution, transparency is key, especially as regulators are casting a sharper eye over financial processes. The Rainbird and Blue Prism end-to-end solution is fully explainable to the end-user – and you do not have to be a data scientist to interpret the results. Every decision it makes comes with a full audit trail, explaining the whole chain of reasoning it went through, all the data used and the various uncertainties involved.

To get a deeper understanding of how Rainbird’s intelligent decision automation and Blue Prism’s RPA combine to deliver end-to-end automation, watch our latest webinar in full here.

Free webinar: Blue Prism + Rainbird integration demo webinar
See a detailed run-through of how Rainbird's intelligent decision automation and Blue Prism's RPA combine to deliver end-to-end automation.

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All change: prepare for post-Brexit shakeup in financial regulations

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Financial institutions hoping for a smooth regulatory Brexit transition will have been dismayed by foreign secretary Dominic Raab insisting that UK alignment with EU laws post-Brexit is “not even on the negotiation table”. The current transition may have provided some stability for UK firms, but in January 2021 an unpredictable new era begins. 

For financial firms, it is difficult to picture what that era will look like: the fourth round of negotiations between London and Brussels ended last Friday with both sides admitting little progress had been made, with the services sector reportedly largely ignored so far. Josh Hardie, deputy director of the Confederation of British Industry (CBI), said that businesses are “unprepared for a dramatic change in trading relations with our biggest partner in just six months’ time.”

Figures from within the industry have voiced their concerns. Barney Reynolds, a lawyer specialising in financial regulation at Shearman and Sterling, told City A.M. it was “inevitable” that the EU would “want to change some of the details in its regulatory regime”. Six in 10 UK financial services workers fear the aftermath of Brexit will be the single biggest challenge the sector will have to face over the next 12 months. 67% said that increasing regulation was already a major challenge in their day-to-day activities. No one can claim to be in the dark about the storm on the horizon. 

Financial firms must now try to find stability within a contorting regulatory landscape. Bloomberg columnist Lionel Laurent writes that expecting anything other than “messy uncertainty” in UK/EU trade talks is “wishful thinking”. Messy uncertainty is indeed what firms will find themselves mired in, unless they can restructure their organisational and technological makeup to maximise their specialist knowledge.

The MiFID mystery 

Uncertainties abound. There is the question of whether UK firms will still need to comply with the standard trading practises of MiFiD II, an EU directive which harmonises investment regulations across EU markets with controls on research spending, record keeping and trading in stocks, derivatives and commodities. Will new and adjusted versions, such as the touted ‘MiFiD 2.5’ regulation, come into play? UK and EU fund managers are still at odds over the Mifid revamp, according to the Financial Times – so few conclusions can be drawn.

Bloomberg reports that officials in Brussels, Berlin and Paris are looking to amend the bloc’s Mifid II financial regulations by walking away from concessions made to the UK when the rules were originally drawn up. There is a danger of the UK financial services industry becoming a political football. “There is a risk that the Mifid review could be misused for political ends, which could ultimately, and regrettably, serve to frustrate access to EU markets by City firms,” Nathaniel Lalone, partner at Katten law firm in London who works on cross-border regulatory issues in the derivatives market, told Bloomberg.

As reported in the Telegraph, city insiders claim the depletion of analyst coverage – sparked by Mifid’s call for more transparent fees – has given rise to an exodus of star analysts, forcing big investment banks to hire “younger, cheaper analyst teams” and settle for poorer coverage.

The unpredictability factor affects legislators as well as businesses: national regulators imposed a last-minute delay to the MiFiD rules for the futures market, pushing back compliance until July 2020 – and blamed uncertainty around Brexit, according to the Financial Times.

Amidst a tide of uncertainty, one fact is abundantly clear: with tough times ahead, financial firms must find ways to capture and productise all of the in-house expertise that they process.

For a closer look at how machine intelligence can help businesses handle regulatory change by capturing and productising expertise, head to our eBook below on avoiding the looming brain drain in financial services. 

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.

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Vulnerability in banking: technology must represent the dangerous ‘grey area’

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Look to your left. Now look to your right. Statistically speaking, at least one of these people is currently financially vulnerable. 50% of UK adults display one or more characteristics of being potentially vulnerable, according to latest FCA statistics.

While it may not always appear obvious on the surface, and though some of us may not be aware of it, we are all on a spectrum of financial vulnerability. Of course, there are degrees of extremity. The worst examples are heartbreaking cases of deceit and neglect: a fraudster exploiting the trust of an elderly Alzheimer’s suffer for £15,000; scam pension schemes taking £13.7 million from their victims.

But beyond extreme cases, there is an ocean of grey area. Depending on circumstance, financial vulnerability could include all of us. Even the subprime crisis that led to the 2008 financial crash could be viewed through this lens; it involved banks granting loans to people who couldn’t afford them. 

The nuanced cases are what banks need to get better at identifying when assessing a customer’s potential vulnerability. Not only for our safety, but also for our financial literacy and financial wellbeing. The demand for better, more personalised financial guidance from banks is increasing; four out of five students say they are not taught enough practical money lessons in schools.

With post-Brexit downturn on the horizon, and news of the latest economic blow courtesy of coronavirus, we may as well call a spade a spade: the economic forecast right now isn’t looking too rosy. Wages could well stagnate, and the cost of living may rise. Many more people could indeed slide further up the financial vulnerability spectrum and be in need of better guidance from their payment providers.

Vulnerable people don’t benefit from blocked transactions and invasive phone calls from bank operatives; they benefit from frictionless transactions and intelligent recommendations. Knowing when and how to contact potentially vulnerable customers is key, and that can only be achieved at scale with a decision-making system capable of identifying the right protocol for the unique contexts of each customer.

For any banks that fail to provide this, there are a host of challenger banks already turning heads with superior personalisation. This is not just about protection – it’s about banks being able to offer the most tailored services possible by understanding a customer’s unique context. 

Current identification methods are too wide

Financial vulnerability is more nuanced than the identification systems and criteria that banks are currently using. Take, for example, a recently widowed spouse whose partner had been in charge of finances. There may well be compounding factors that should all accumulate into a bank’s assessment of vulnerability: a lack of financial literacy; age; any possible illness or disability. This is what we call compounding vulnerability. This person, likely to be under great stress, needs to make the transition from a joint account to a checking account. The bank has a duty to check for vulnerability – as all banks do.

But what banks are missing is that they also have a duty to relieve, rather than add to, this recently bereaved person’s stress levels.

Rather than asking a series of questions ranging from date of birth to net income, there needs to be a system in place that asks fewer, more explicit questions to get the information it needs to assess the level of vulnerability of the customer. 

Yes, identification processes are currently in place, but they are not refined enough to be able to remove stress and friction from customer interactions. We see the same problem with credit card fraud; companies use linear automation tools without the nuance to identify the ‘grey area’ scenarios, and end up flagging far too many cases. 

This trend was emphasised in the Scotsman’s Talking Money report: “the industry thinks it’s fair – but it’s also very wide. There could be actual vulnerability, where a customer has the inability to do certain things day-to-day, or it could be transient, like someone losing their job or being unable to work due to illness, for example. A lot of banks place their focus on debt management rather than making things simpler and accessible for customers.” 

Machine intelligence can identify the full spectrum of vulnerability, and tailor services accordingly

Vulnerability is context-specific and rarely black and white. The kinds of linear technologies being widely used today to handle potential vulnerability cases can’t calculate this – but people can.

Banks provide specialist training to staff to identify different customers’ wants, needs and vulnerabilities, and to do so with nuance. This is specialist expertise that could find its most effective use in identification systems. 

All of which means that we need to find a way to capture the human understanding that people possess, and translate it to technology.

Whether you’re 10% or 40% vulnerable, your payment provider should know what’s best for you.

For a deeper dive into humanising our financial services, I recommend the latest Rainbird eBook on financial vulnerability, in collaboration with Ernst & Young and the Royal College of Arts.

Recognising and supporting vulnerable customers in banking
In the wake of the COVID-19 crisis, the number of vulnerable people in every society has skyrocketed. This comprehensive ebook explains the definition of vulnerability and its different forms, why it's unwise for banks and financial institutions to ignore vulnerable customers, and why intelligent decision automation is the game changer we all need.

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Rainbird and EY support RCA students to develop AI banking apps that help vulnerable customers

The Royal College of Art
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Rainbird, the intelligent automation platform, and global consultancy Ernst & Young (EY), have collaborated with the Royal College of Art (RCA) to offer industry knowledge and expertise to students enrolled on the RCA Service Design Programme course. The aim of the course is for the students to develop cutting-edge AI-enabled apps that will help vulnerable banking customers better manage their finances and navigate the complex financial services industry. With an expected increase in the number of people classified as a ‘vulnerable customer’, following the severe economic impact of the coronavirus pandemic, there has never been a more pressing time to help those struggling to navigate the financial services landscape. 

The students conducted face-to-face interviews with people categorised as vulnerable, to ensure a human-centred approach to the AI solutions they created. The apps developed by the groups demonstrate there is far more to be done to protect vulnerable people, when it comes to banking. They also covered a wide range of vulnerability use cases and value propositions, including:

  • A preventive service that applies consumer gamification and incentives to nudge users towards positive behavioural changes. The service is designed to use Rainbird to generate a “wellbeing score” that helps banks recognise those who need extra support. 
  • A service aimed at preventing financial abuse by improving the joint account service for couples. One in five adults is a victim of financial abuse (most of these being women). The service is designed to use Rainbird to identify potentially vulnerable people by analysing suspicious transaction patterns, unusual behaviour and words used in help centres. 
  • A service designed to boost financial literacy among young people, whom are highly represented among vulnerable customers. The service would use Rainbird to identify the potentially vulnerable, based on multifactorial signs of low resilience (e.g. low savings or over-indebtedness), and deliver automated, contextual advice.

James Loft, COO at Rainbird, said, “As banks are increasingly tantamount to online businesses, the nuanced and careful consideration of a customer’s circumstances should be incorporated within technologies used to assess vulnerability. The complex factors that influence a bank’s assessment of transient or compounding vulnerability can be reliably handled only by technology with human knowledge at its core.”

The students presented their proposed apps to a distinguished panel and detailed the resources needed to bring their ideas to life, a clear timeline and implementation budgets. 

The proposed solutions align with guidelines set by the Financial Conduct Authority (FCA) on how to assist vulnerable people, and comply with GDPR.

Clive Grinyer, Head of Service Design at RCA: “At the RCA Service Design course, we apply design approaches and methodologies to some of the biggest problems facing society and businesses. The opportunity to work with Rainbird, to develop new ways of identifying and helping people in vulnerable circumstances, fits perfectly that ethos. Our students have the opportunity to experiment and shape how AI and financial services can create real solutions to these problems.”

Chris Withers, Head of AI and Advanced Analytics, Financial Services at EY: “Improving outcomes for society’s most vulnerable citizens is an issue that will be even more urgent as a result of the Covid-19 pandemic. There are no simple solutions to improve a person’s life situation or break a cycle of financial abuse or neglect, but there are defining moments where data, information and technology can come together to provide benefits to those people who are more vulnerable at any given point in time. The RCA students have designed solutions which demonstrate the art of the possible for all of us who participate in the financial services sector.”

You can download below our report on vulnerability in banking.

Recognising and supporting vulnerable customers in banking
In the wake of the COVID-19 crisis, the number of vulnerable people in every society has skyrocketed. This comprehensive ebook explains the definition of vulnerability and its different forms, why it's unwise for banks and financial institutions to ignore vulnerable customers, and why intelligent decision automation is the game changer we all need.

Join the Newsletter

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.

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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.

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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.

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81% of UK businesses say a shortage of talent is the biggest hurdle to AI adoption

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The research, conducted by Vanson Bourne on behalf of Rainbird, surveyed senior decision-makers in enterprise organisations. It found that the main reason behind businesses not implementing AI is a shortage of talent in their workforce for handling automation processes. While this was the overall biggest barrier to adopting AI in the UK, when broken down into professional services, financial services, insurance and IT, the data highlighted a number of different concerns across business functions.

View the full report.

James Duez, CEO at Rainbird, commented: “In order to truly understand what processes will benefit from AI, businesses must review their strategies. Rather than pushing AI investment into IT departments, organisations should recognise where the most important decisions are being made – within the business. Symbolic tools are business-friendly, rapid to work with and completely auditable and it is these that will unlock the streamlining and automation of operational decisions.”

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Neural networks can disempower human workers: the case for human intervention amidst rapid AI adoption

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Neural networks have become the alchemy of our age, the search for a magical, mystical process that allows you to turn a pile of data into gold. It is widely seen as a silver bullet that can generate new insights and expert decisions on an unprecedented speed and scale. Yet this ignores the reality that ‘deep learning’ systems are difficult to create or audit and most organisations lack the necessary in-house expertise or ‘data hygiene’ to use it effectively.

To read the full article, see Neural networks can disempower human workers: the case for human intervention amidst rapid AI adoption on Digitalisation World. 

 

 

 

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Ghosts in the Machine: How Machine Learning is Transforming Business

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According to new research from Rainbird – the AI-powered automated decision-making platform, 81% of those surveyed revealed that their organisation planned to increase investment in AI over the next five years. Of those who plan to increase spending on automation technologies, 22% suggested this investment would be significant. Interestingly, the financial sector is set to be the biggest adopter, with 94% of those surveyed planning to increase investment in AI over the coming years.

James Duez, CEO at Rainbird, commented: “AI should be brought into organisations to help employees, not hinder them. UK organisations – and beyond – need to fundamentally change the way they are adopting AI and, think beyond big data and machine learning. ‘Data scientists only understand black box solutions, and there are huge benefits to be had by moving towards more transparent symbolic technologies which can achieve automation outcomes beyond those available with data-only approaches. Such accessible tools also have the added benefit of addressing the skills gap by making AI far more accessible to employees without a degree in data science.”

To read the full article, see Ghosts in the Machine: How Machine Learning is Transforming Business on Silicon UK.

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.

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