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HOW AND WHEN TO ADOPT AI WITHIN FINANCIAL SERVICES

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Ash Finnegan, digital transformation officer at Conga 

 

Many firms across the financial services sector have felt the pressure to ‘digitise’ over the past decade or so, in order to keep up with consumer demands as well as the rise of fintech rivals and the latest challenger banks. As revealed in a joint report by the Alan Turing Institute and Bank of England, institutions have felt the need to invest in the latest artificial intelligence (AI) and automation solutions in order to stay competitive, and machine learning (ML) adoption has increased considerably since 2008.

Back in 2019, two thirds of companies reported they already used ML in some form, and this was expected to double over the next three years.1 However, the pandemic has brought a new sense of urgency. In fact, according to The World Bank, COVID-19 has completely changed how consumers interact with financial services and banking providers, with digital asset exchanges and online payments increasing by as much as 13 percent.2 Similarly, as a result of social distancing measures, many customers were unable to visit local bank branches, which placed a heavier burden on contact centres and core systems.

Out of necessity, digital transformation journeys across the sector have accelerated exponentially, with automation being more of a focus. Financial leaders have invested heavily in artificial intelligence and wider automation technology, digitalising customer channels and entirely restructuring their back office in order to deliver their services remotely – chaos has been the driver of change. In fact, an earlier report by IDC revealed that 83 percent of banks in EMEA are still focused on business continuity and building resilience into their operations.3 However, that does not mean that each of these automation programmes have been well executed or delivered effectively.

 

How to approach automation  

Many financial leaders have struggled when it comes to scaling AI technologies across their organisation. As a report by McKinsey revealed, the most common obstacles hampering banks’ automation efforts, are the lack of a clear strategy for AI, weak core technology and data backbone, and an outmoded operating model.4 In general, initiatives are rushed or short-sighted, and COVID-19 has only accelerated this issue. As Conga’s own research indicates, whilst the pandemic has accelerated 71 per cent of companies’ digital transformation plans, only 36 per cent of these initiatives have proven successful.

Most aspire to be disrupters, picking a technology and implementing it at speed in order to keep up with competitors. They want to adopt the latest AI programme, be that robotics process automation (RPA) or natural language processing (NLP), with no real idea of how this will improve their overall services or operational model. Whilst AI offers many competitive advantages, that does not necessarily mean it is easy to implement or deliver as part of a wider transformation project.

AI is only as good as the data provided and if there are bad processes in place, particularly between departments – whether contracts or loans managed by the sales and legal teams – automation will only accelerate this issue.

 

Establishing ‘digital maturity’ – when and how to adopt AI  

Before considering any new or transformational technology, firms need to review their current operational model and establish where they currently stand in their own digital transformation journey, by considering their own digital maturity. Given the speed at which most institutions adjusted to remote working last year, departments may have stumbled across a number of bottlenecks or unnecessary processes, and this will have affected overall workflow.

By taking a step back and reviewing the operation model, leaders will have a clearer picture of the current state of their business, and what the next stage of their digital transformation journey should be. After identifying any operational issues and reviewing legacy systems, leaders can then establish clear objectives, whether that is improving customer service and speeding up response times, or unifying systems of record and streamlining data flows between teams. Only then can leaders consider incorporating AI or automating elements of their business, streamlining the processes that matter and helping them to achieve these goals.

As institutions proceed along their digital transformation journeys, they will streamline processes, break down silos and enable cross-team working across departmental boundaries. It is vital that at every stage of that journey, leaders fine-tune the basic workflow to ensure any inefficiencies are removed.

 

AI is not a silver bullet or a ‘quick fix’ 

If companies think automation will solve all their problems, they are approaching transformation all wrong. Financial services firms, just like any other business, need to fully optimise their commercial operations process, before considering any new technology. It is crucial that leaders and IT teams establish the company’s digital maturity – where they are and where they need to get to – and review the operational model throughout every stage of their digital transformation journey, from foundation to full system integration.

Furthermore, they need to consider each phase of their operations – front to back office. By streamlining their operational model and unifying systems of record, companies will have far greater insight into data streams, and this will empower AI, taking their business to a true state of intelligence.

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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Banking

How Banks Can Boost App Innovation, Speed and Compliance

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Steve Barrett, Senior Vice President of International Operations, Delphix 

As new finance and banking applications disrupt the market each day, and customer expectations around speed, privacy and quality continue to grow, financial organization CIOs and DevOps teams have to innovate quickly to bring new apps and updates to market, while remaining strictly compliant to a myriad of regulations. DevOps innovation in financial services requires fast access to accurate, compliant test data, and as anyone who touches the industry knows, data privacy is a highly complex, critical process woven into the everyday world of finance.

Banks and financial services organizations collect vast amounts of data, but using that data for innovation can be challenging due to the vast size and complexity of test data. These challenges can inhibit the adoption of new and transformative technologies and hinder innovation if they are not addressed head on. To address these challenges, many organizations are integrating the use of highly innovative test data management (TDM) tools within their DevOps ecosystems. DevOps TDM provides access and delivery of lightweight, compliant data for DevOps initiatives including digital transformation, software upgrades, cloud migration, artificial intelligence and machine learning (AI/ML), and analytics.

Data – the last automation frontier

Historically, application teams manufactured data for development and testing in a siloed, unstructured fashion. Over time, large IT organizations began consolidating TDM functions to take advantage of innovative tools to create test data. With the rise of modern development methodologies like DevOps and CI/CD that demand fast, iterative release cycles and end-to-end API-driven automation, legacy TDM approaches are often no longer sufficient.

Reliance on a traditionally manual, ticket-driven, request-fulfill model creates time drains during test cycles and slows the pace of application delivery. Consider the payments industry, in which agile technology companies using optimized DevOps processes can release new code hundreds of times per month. In contrast, traditional banks with slow IT ticketing systems may take months to release new features. These manual, legacy TDM approaches exist in contradiction with modern DevOps practices and CI/CD processes that depend on automation and fast feedback to development teams.

TDM for the DevOps Era

DevOps teams rely on TDM to evaluate the performance, functionality and security of applications. However, while processes including storage, compute, and code have all been automated, data has eluded the reach of most DevOps toolchains.

Now, DevOps TDM can help accelerate app releases and increase compliance.by automating the delivery, provisioning, and compliance of data. These practices provide both development and testing teams with data APIs, including the ability to refresh, rewind, bookmark, group, tag, branch, and share test data, to accelerate DevOps productivity and improve application quality. DevOps TDM also includes copying production data, and the masking (anonymization) and virtualization of data through the DevOps pipeline, which helps accelerate app releases and increase compliance.

And as the pace of application development quickens, so does the pace of privacy regulations and efficiently ensuring compliance in DevOps has become a significant challenge for enterprises. Non-production data used for testing software applications, reporting, and analytics can contain up to 80% of an enterprise’s sensitive data. To solve this, DevOps TDM provides integrated data masking to de-identify personally identifiable information (PII) and other sensitive data in non-production environments, eliminating the risk of sensitive data exposure.

The World Quality Report 2022-2023[1] by Capgemini stressed the importance of an enterprise wide approach to test data provisioning (a core component of TDM). The report states, “Over the years, with stringent regulatory and security requirements around data, organizations have increased their focus on provisioning test data safely and securely.”

The report shows that secure test data provisioning remains a challenge, with only 20% of respondents having a fully-implemented enterprise test data provisioning strategy in place to address security and compliance requirements.

Data is the catalyst to innovation

Automation is fueling myriad digital transformations within the financial services sector, but without the right data, these application innovations cannot succeed. DevOps TDM can help further accelerate DevOps initiatives by automatically delivering fresh, complete, and secure test data wherever and whenever it is needed, in minutes. With DevOps TDM, banks and financial institutions can innovate faster, reduce time-to-market for updating legacy applications, and accelerate development and testing of disruptive fintech.

 

[1] Source: https://www.capgemini.com/insights/research-library/world-quality-report-wqr-2022/

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