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MAKING THE MOST OF RPA TO ENHANCE THE CUSTOMER EXPERIENCE

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Standfirst: Capturing and analysing business processes should be a prerequisite for any implementation of robotic process automation, argues Dr Gero Decker, General Manager SAP Business Process Intelligence & Co-founder of Signavio

More and more companies are turning to end-to-end digital transformation as a means of improving customer experience (CX) and propelling growth.

Dr Gero Decker

Several automation technologies are helping to drive this, from digital process automation and dynamic case management to artificial intelligence and low code. Among this group of transformational tools is robotic process automation (RPA).

In simple terms, RPA involves using a piece of software to complete a specific task that traditionally is carried out by a human worker, the concept emerged in the early 2000s and has been on an upward trajectory in recent years.

In 2019 the RPA market registered $1.4 billion in revenue, a figure that is set to rise to almost $2 billion for 2021 according to Gartner, which cites the disruption caused by the COVID-19 pandemic as a key accelerant of the shift from manual to automated business processes.

Before the pandemic erupted in March 2020, many companies were reliant on paper-based and entirely human-powered operations, a dynamic that has been uprooted as remote-working became mandated for large swathes of the workforce.

Meanwhile, a transformation in how customers and businesses interact has also occurred.

According to McKinsey, the average share of customer interactions which were digital across the globe stood at 58% in July 2020. This is significantly higher than the 36% reported in December 2019 and represents a three-year acceleration in the space of just a few months.

Momentum is continuing to build. For instance, some 98% of IT business leaders believe automating processes is essential to sustaining business success, while almost half of jobs could be automated within the next 20 years.

 

How to make RPA work for your business 

In other words, more organisations are coming round to the idea that time-consuming and repetitive business processes can be automated, especially those which human workers find difficult, mundane, or frustrating to complete. In particular, repetitive, standardised tasks that are based on specific rules and high priority.

Automating these tasks can deliver significant cost savings, minimise errors and open a host of other benefits which ultimately lead to an enhanced customer experience.

However, implementing robotic process automation effectively is often more challenging than initially anticipated, with around half of all RPA initiatives failing to deliver expected improvements due to poor execution.

So, what is preventing businesses from making the most of the opportunity?

A major cause is overlooking the ‘process’ element of RPA. This leads to the automation of systemically poor processes, the likely result being that the same mistakes are made faster and on a grander scale.

This issue stems from a disparity between what business leaders think is happening and what is occurring, and it is only by aligning the actual and desired states prior to implementation that RPA can be successful.

Companies should therefore begin by framing action around a simple question – what processes which could be appropriate for automation are suboptimal?

This is part of an evaluation exercise we refer to as ‘capturing’ processes, which can be conducted using automated process discovery tools and insights from process participants.

Forward-thinking businesses may already be utilising dedicated experts (either in-house or via a third-party partner) to discover and analyse processes at scale through process mining, leveraging data from programs such as Salesforce.

After business processes have been analysed, decision-makers will be able to make informed judgements as to where RPA can best be deployed to enhance CX. What’s more, these decisions will be underpinned by a sound, data-backed business case.

A comprehensive understanding of the customer is also essential if any process capturing exercise is to be worthwhile.

Customer journey mapping can track interactions all the way from the first touchpoint to post-sale activities such as deliveries, returns and feedback gathering – combined with internal insights, these findings help organisations to provide superior, more personalised services to each of their target audiences. Process capturing and analyses must therefore be grounded in this level of customer understanding.

 

The human-machine dynamic

As well as transforming customer experiences, RPA has clear ramifications for many staff roles.

There is much talk about the ‘human versus machine’ paradigm here, commonly framed around fears that robots will displace human jobs and leave large numbers of employees out of work.

If implemented properly, RPA should not project this message. Rather, businesses should leverage automation as a means of augmenting and upskilling staff, not displacing them.

While RPA is undoubtedly a savvy option for handling large volumes of data and mundane tasks at speed, there will be times when a process requires a human assessment – indeed, a central message behind any RPA strategy should be that technology is not replacing human insight and effort.

And the more a business automates, the harder it gets to fix broken automations. By retaining process experts after full automation is adopted, disruptions to customer experience can be minimised when problems emerge.

By augmenting human expertise and paying due attention to capturing and analysing business processes before implementation, organisations will be well placed to leverage RPA for the benefit of their business and customers.

Business

Ransomware chokes COBRA: How AI-powered data analysis can support financial services’ plight

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By Toby Butler, Financial Crime Solutions Manager at Ripjar

 

Ransomware attacks are on the increase in the United Kingdom. Most of the British Government’s COBRA meetings have been convened in response to ransomware attacks, showing how cybersecurity breaches are as pressing as national emergencies and crises. The National Cyber Security Centre’s (NCSC) annual review found this year that the country was hit by 17 ransomware incidents that were so impactful they “require a nationally coordinated response”. That extends to the financial services sector, which saw an increase of ransomware attacks with 55% of organisations hit in 2021.

Where does this leave the sector and how can artificial intelligence and machine learning be instrumental in understanding the risks companies face against future ransomware attacks?

Toby Butler

Company information is being stolen and sold to different threat groups, who prey on the individuals in that organisation who are more likely to pay them. The UK is one of the most cyber-attacked countries in the world and the Government has been criticised for being “ill-equipped” to deal with this exponential rise of fraud cases.

 

Ransomware-as-a-Service

Ransomware is one of the most common forms of cybercrime. Fighting it has become one of the biggest problems that organisations today face during their everyday operations. For instance, Malware (malicious software) encrypts the files of a single computer, then works its way through an entire network to reach the server and inflict maximum damage. Company information is being stolen and sold to different threat groups, who prey on the individuals in that organisation who are more likely to pay them.

When these attacks occur the victims, more often businesses, are left with minimal options. If they have substantial backup solutions already in place, they can attempt to restore the encrypted data to their servers. But if that data isn’t already secured elsewhere, they may need to pay a ransom to the criminals behind the attack. Thereby allowing the business to function once again and restoring their reputation. The cost of paying the ransom will feel considerably smaller compared to starting a business again from scratch. Sophos’ State of Ransomware in Financial Services 2022 report found that 52% of financial services organisations paid the ransom to restore their data, the average remediation cost in financial services was US$1.59M.

Cybersecurity Ventures estimates that ransomware is set to cost global businesses more than $256 billion by the end of 2031. By that token, organisations need to be extremely mindful of the potential threats they may face. Businesses need to understand the methodologies these hackers use, to address the weaknesses within their domain and take measures to isolate and prevent further ransomware attacks from happening again.

 

The rise of WAMs

According to a recent report by security firm CyberSixgill, 19% of the 3,612 cyberattacks that took place in 2021 were traced back to Wholesale Access Markets – or WAMs for short. WAMs are, in essence, underground internet flea markets. These markets are where aspiring attackers come to purchase network access from threat actors – the individual or entity involved in carrying out the cyber-attack. Types of threat actors include insiders, cybercriminals, rival organisations, or even nation states stealing data.

WAMs sell access to multiple compromised endpoints (or pathways) for around 10-20 dollars. Researchers found that WAMs listed access to approximately 4.3 million compromised endpoints in 2021, which include access to both provider and enterprise software (for example, an organisation’s Slack channel) up to 180 days before the attack itself took place. This shows how long these compromised endpoints remain undetected without proper internal analysis.

 

How can Financial Services stay ahead of the curve?

The use of Artificial Intelligence (AI) and machine learning is undisputed across modern businesses and sectors, and continues to revolutionise processes across the board. AI is a significant player in the financial services industry, building the ‘cyber-wall’ against nefarious users. It gives organisations optimal insights into reducing the likelihood of a ransomware attack in the future.

Namely, AI and machine learning collects and analyses vast amounts of messy (structured and unstructured) data from disparate sources. The challenge for the sector is to understand the volume and variety of the raw data collected from any source to build better protection in the future.

Structured information could be best understood as the clear data we see in a table. For example, the following attendees made a business meeting: first name – Joan, surname – Smith, age – 46. But unstructured information is information presented in a complex manner. For example, ‘there were five people who attended the business meeting, one of whom was forty-six and called Joan Smith’. Naturally, due to the complex nature of the prose, it would be more difficult for a machine to process that data into a digestible format for further risk analysis. This is where AI continues to prove invaluable.

AI uses natural language processing to understand the information provided on the web. As the software continues to evolve, natural language processing reads the information in a way a human would to extract the key information from the text. By incorporating AI and machine learning within an organisation’s IT infrastructure, companies operating within financial services can be better equipped to handle cybercrime.

These tools are flexible and adaptable, they can be configured to analyse different types of data from different sources to curate key insights. This collated information provides a better analysis of the organisation’s exposure, allowing them the opportunity to get upstream in preventing future attacks. This kind of approach is essential to processing listings on WAMs.

The power to analyse data to identify weakness is vital in the battle against cybercrime. It gives organisations a better understanding into what they could expect to see in the future. Hosting the correct data, and with the analytical skills, financial organisations can gain a better understanding of the methodologies and weaknesses in-house that attackers use and exploit to hold them to ransom. Organisations can then use this as a reference to pinpoint compromised endpoints, giving them a chance to reduce access before this route can be exploited and ruin their business.

With cybercrime and ransomware continuing to remain prevalent, it’s vital that financial services companies understand how they can get ahead of the curve and build a robust security platform within their IT infrastructure that can withstand an attack. In 2022, a ransomware attack occurred every 40 seconds. The mindset for the sector needs to be one of when, not if.

Organisations need to be thinking about an attack now – before it’s happened. Pre-planning and preparing for the worst possible outcome from future threats and adversaries. The introduction of AI and machine learning in the fight against cybercrime is a must, and the sooner the industry gets behind in implementing AI, the safer it will be through the next decade.

 

 

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SVEA BANK ACQUIRES AREX’S FINTECH OPERATION IN FINLAND

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AREX Markets, the data-driven FinTech company that drives financing costs down for SMEs and enables them to get paid quicker, has announced the sale of its Finland operations to Swedish payment and financing institution Svea Bank.

With the deal, Svea will further strengthen its position as a corporate financier, as AREX’s c.1200 Finnish customers and partnerships in the areas of financial management and financial management software will be transferred to the bank’s portfolio. The Finnish operation of AREX has financed over EUR 500M worth of invoices.

AREX’s Spanish and UK operations remain unaffected and remain focused on building embeddable financing products for third party platforms. Customers in Finland have been informed of their transition, and their contracts and service details will port across to Svea.

Svea is reshaping the playing field of corporate finance in Finland, and taking on the operations of AREX in the region is a natural step to strengthen their own business and at the same time offer AREX’s partners and customers an easy path to a wider range of services than before.

“Over the years, Svea has grown a lot also through business transactions, therefore acquiring AREX’s business operations in Finland was a good and natural solution for us. In addition, the deal is pleasant for us at Svea because the focus of our activities is to help partners and customers succeed – offering AREX’s partners and customers a wider range of services is exactly that,” says Pasi Väre, country manager of Svea in Finland.

The deal also brings new opportunities for AREX to focus on the UK and Europe in its roll out of embeddable financing products, which can be white-labelled by neobanks, ERPs and accounting software alike. The business is seeking to bridge the liquidity gap faced by most small businesses in the face of a recessive economic climate.

UK SME’s can continue to access AREX’s core invoice financing product through the Xero marketplace.

“For us at AREX, this is a great step: we are developing a stronger presence in the field of embedded finance, which is underpinned by our sophisticated marketplace software, our strongest point,” says AREX’s CEO, Airto Vienola.

“For the AREX team it was extremely important that we find the best possible corporate financier to take care of the business’ customers and partnerships in Finland. Svea convinced us with their customer and partner-centric approach”, adds AREX’s co-founder Perttu Jalkanen.

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