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INETCO WHITEPAPER – UNLOCKING YOUR ATM BIG DATA

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INETCO WHITEPAPER – Unlocking Your Atm Big Data (Or: How I Learned To Stop Worrying And Love ATM Big Data)

by Brad Zaytsoff

Have you been to a conference recently where they had a session on Big Data?

Have you watched a webinar or downloaded a whitepaper about unlocking your Big Data?

Are you looking for a little less talk about why you need Big Data, and a little more information on HOW you can derive actionable intelligence from it?

Good—because in this week’s post you’ll learn how you can easily access your ATM Big Data, and how it can provide actionable intelligence to lower operational costs, generate more revenue, and improve customer experience.

If you are in retail banking it is likely that you are investing in more sophisticated, interactive ATMs to handle a greater number of transactions traditionally carried out by costly tellers (mortgage applications, account balance inquiries, check deposits…). These expanding services and the growing number of customers choosing self-service options are resulting in an explosion of (big) data available from your ATM network. The key is finding a cost effective way to access this data, a method that doesn’t require you to assemble a war room of specialists and data extraction tools before you are able to generate actionable intelligence.

Fortunately, a real-time transaction monitoring and analytics solution can help retail banks and credit unions easily access and process ATM transactional data so they can make better and more timely decisions. This type of banking software platform captures the Big Data flowing across your ATM networks and provides a centralized deposit of rich transaction information. Its analytics engine enables you to produce customized statistics and key performance indicators—actionable operational and business intelligence that allows you to:

Better serve your ATM customer:

  • Analyze uptime and availability statistics specific to ATM, switch, 3rd party service connections, and card type
  • Generate automatic reports on number of incidents reported by end customers
  • Understand customer usage by card type,  service type and ATM terminal location

Reduce ATM support costs:

  • Track payment failure rates by root cause
  • Analyze approved/declined/failed transactions by card,service type or ATM terminal ID in real-time
  • Quickly assess the direct business cost associated with system downtime and loss of business

Improve ATM channel profitability:

  • See hourly withdrawal and deposit transaction information to pre-emptively respond to cash-low events for busy ATMs and new cash point scenarios
  • Create marketing campaigns based on which competitor cards are using your ATMs, “on us” versus “off us” transaction comparisons, and the most popular value added services
  • Get a breakdown of customer transactions by ATM such as withdrawals, deposits, inquiries, bill payments, transfers and other value added services; understand failed customer transactions by value of service revenue lost

INETCO WHITEPAPER – UNLOCKING YOUR ATM BIG DATA 3Transaction monitoring and analytics solutions provide access to a wide range of customized ATM service and cash management statistics to help retail banks manage targeted campaign performance, enhance the profitability of their ATM channel, and ensure important customer interactions are secure and reliable. ATM channel managers and marketing teams will learn to love Big Data as it helps them make better business decisions regarding ATM locations, service offerings and card bases. Operations teams will be able to produce a wider array of customized alerts and performance statistics that lead to improved service availability and reduced support costs. Granular usage information such as which transactions were more common at a particular ATM, how much service revenue was produced, how many banking transactions were successful, or how many were unsuccessful provide a centralized easy way to listen to how your ATM network is performing.

For more information on how you can generate actionable intelligence and learn to love your ATM Big Data, download this complimentary whitepaper

Banking

What banks need to know about observability

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What banks need to know about observability 4

By Abdi Essa, Regional Vice President, UK&I, Dynatrace

More aspects of our everyday lives are taking place online  from how we work, to how we socialise and, crucially, how we bank. To keep pace, financial organisations have stepped up their digital transformation efforts, supported by a shift to dynamic multicloud environments and cloud-native architectures. However, traditional monitoring solutions and manual approaches cannot keep up with these vast, highly complex environments. As a result, many banks are turning to new, observability-based approaches to understand what is happening in their digital ecosystems. These approaches, however, bring new challenges to overcome.

Here are six things banks need to know about observability to ensure they can gain true value, combat the complexities of their modern multicloud environments, and drive digital success in 2021 and beyond.

  1. Most banks have very limited observability

The scale, complexity, and constant change that characterises hybrid, multicloud environments presents a real challenge to banks’ IT teams. Our research found that, on average, banking digital teams have full observability into just 11 percent of their application and infrastructure environments – not nearly enough to understand what is happening, and why, across the digital ecosystem. Additionally, 87 percent said there are barriers preventing them from monitoring a greater proportion of their applications – including limited time and resources. Without improving observability across the entire cloud environment – by drawing in metrics, logs, and traces from every application – banks’ IT teams are limited in the success they can have driving initiatives to deliver the new banking products and quality user experience customers want.

  1. You can’t bank on manual approaches

With many banks beginning to rely on more dynamic, distributed multicloud architectures to deliver new services, IT teams are stretched further than ever. More than a third of financial services organisations say their IT environment changes at least once per second, and 65 percent say it changes every minute or less. This rate of change creates a volume, velocity, and variety of data that has gone beyond banks’ IT teams’ ability to handle with traditional approaches – there’s no time to manually script, configure, and instrument observability and set up monitoring capabilities. The need for automation is therefore critical. By harnessing continuous automation assisted by AI in place of manual processes, teams can drastically improve observability to automatically discover, instrument, and baseline every component in their bank’s cloud ecosystem as it changes, in real-time.

  1. Cloud native adoption is obfuscating observability

To remain agile and keep up with the rapid pace of digital transformation, banks are increasingly turning to cloud-native architectures. Our research found 81 percent of them are using cloud-native technologies and platforms such as Kubernetes, microservices and containers. However, the complexity of managing these ecosystems has made it even harder for banks’ IT teams to maintain observability across their environments. Nearly three-quarters of banking CIOs say the rise of Kubernetes has resulted in too many moving parts for IT to manage, and that a radically different approach to IT and cloud operations management is needed. Such an approach should be based on a solution that is purpose-built to auto-discover and scale with cloud-native architectures.

  1. Data silos result in tunnel vision

To boost observability, many banks have simply thrown more tools at the problem. Our research found that most organisations use an average of 11 monitoring solutions across the technology stack. However, more isn’t always better, and multiple sources of monitoring data can result in fragmented insights. This fragmentation makes it harder to understand the full context of the impact that digital service performance has on user experience and unravel the nearly infinite web of interdependencies between banks’ applications, clouds, and infrastructure. Instead, financial organisations should seek a single platform with a unified data model to unlock a single source of truth. This will be integral to ensuring that all digital teams are on the same page, speaking the same language, and collaborating effectively across silos to achieve business goals.

  1. Observability alone is not enough

Simply having observability doesn’t help banks achieve tangible benefits or reach their business goals. To get true value, the data processed must be actionable in real-time. As such, observability is most effective when paired with AI and automation. This observability enables teams to instantly eliminate false positives, prioritise problems based on the impact it will have on the wider organisation, and understand the root cause of any problems or anomalies so they can resolve them quickly. The alternative is to manually trawl through dashboards and data to find insights, which is incredibly time-consuming and makes it almost impossible to act in real-time. Our research found that 94 percent of CIOs think AI-assistance will be critical to IT’s ability to cope with increasing workloads and deliver maximum value to the organisation. AI is clearly no longer just a ‘nice to have,’ but a business imperative.

  1. Observability isn’t just for the back end

Far from just having observability of their multicloud environments, banking IT teams also need to be able to see how the code they push into production impacts the end-user experience, and how that in turn affects outcomes for the business. This is a major goal for many CIOs, with 58 percent citing the ability to be more proactive and continuously optimise user experience as a benefit they hoped to achieve from increased use of automation in cloud and IT operations. By harnessing automatic and intelligent observability, banks’ digital teams can unlock code-level insights and precise answers to their questions about user experience and behaviour, so they can continuously optimise their banking services.

Observability is key for modern financial organisations looking to accelerate their digital transformation. By understanding these six key things about observability, IT teams will be better placed to master dynamic, multicloud ecosystems, and drive better digital banking services for the business and its customers.

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Banking

Hackers can now empty out ATMs remotely – what can banks do to stop this?

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Hackers can now empty out ATMs remotely – what can banks do to stop this? 5

By Elida Policastro, Regional Vice President for Cybersecurity, Auriga

In 2010, the late Barnaby Jack famously exploited an ATM into dispensing dollar bills, without withdrawing it from a bank account using a debit card. Fast forward to the present day, and this technique that is now known as jackpotting, is emerging as a threat and is growing as an attack on financial services. Recently, a hacking group called BeagleBoyz in North Korea have caught the attention of several U.S. agencies, as they have been allegedly stealing money from international banks by using remote hacking methods such as jackpotting.

The reality behind jackpotting

Jackpotting is when cybercriminals will use malware to trick their targeted ATM machine into distributing cash. As this criminal method is relatively easy to commit, it is becoming a popular tool for cybercriminals, and this trend will sure continue in 2021, unless financial organisations implement policies to prevent this and protect consumers.

During this difficult time, when access to cash has never been more important to banking customers, it is imperative that banks give their customers reliable ATMs that work, 24/7, 365 days a year. However, due to the sensitive data that ATMs possess, such as credit card or PIN numbers, they have now become a profitable object for cybercriminals to manipulate. As cybercriminals have been evolving in their efforts of attacking the IP in ATM machines, we will definitely see more jackpotting stories emerge in the coming months, especially with the large return on investment.

How criminals exploit the vulnerabilities found in ATMs

Since ATMs are both physically accessible and found in remote locations with little to no surveillance, this gives an opportunity for criminals to carry out jackpotting, especially with the software vulnerabilities that may exist in many ATMs.

ATM machines have been easily manipulated due to the outdated and unpatched operating systems that they run on. If banks wanted to resolve this issue and update these systems, it would take large amounts of time and money to do so. However, some banks do not have such resource and because of this, cybercriminals take advantage by penetrating the software layers in ATMs and exploiting the hardware to dispense cash.

How can banks tackle this?

As the sector has a complex technical architecture, banking organisations will have to make sure that they have control over the transactions that take place, and this includes the management of security when it comes to communication between various actors. When financial organisations are reviewing their ATM infrastructure, they will also need to protect their most vulnerable capabilities within their cybersecurity. Banks, for example, can encrypt the channels on the message authentication, in the event bad actors try to tamper with their communications.

Because ATM networks need to be available 24/7, banks not only, need to implement greater protection over their systems, but they need to do so with a holistic approach. One action that banks can take is to implement a centralised security solution that protects, monitors and controls their various ATM networks. This way banks can control their entire infrastructure from one location, stopping fraudulent activities or malware attempts on vulnerable ATMs.

Another way for banks to reduce the risk of jackpotting attacks is to update their ATM hardware and software. To do this, they will need to closely monitor and regularly review their machines in order to spot any emerging risks.

What the future holds for the banking industry

As confirmed by the warnings from the U.S. agencies, jackpotting remains a very serious threat for financial organisations. Evidence has also emerged, which shows hackers are becoming more innovative in their tactics. It was reported last year, for example, that hackers stole details of propriety operating systems for ATMs that can be used to form new jackpotting methods.

The emergence of jackpotting highlights the need for banks to actively work to protect their customers’ personal information and critical systems now and for the foreseeable future. In order to stay secure and reduce the risk of attacks, they will need to put in place the aforementioned solutions, which include updating their ATM hardware and software as well as closely monitoring and regularly reviewing their ATMs. As cybercriminals continue to become more innovative in their ways of attacking the machines, the issues mentioned will only continue to rise if they are not addressed. Although the method of jackpotting requires little action from cybercriminals, if financial organisations can implement a layered defence to their ATM security, they can stop themselves from becoming another victim to this type of attack in the future.

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Banking

SoftBank Vision Fund set for new portfolio champion with Coupang IPO

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SoftBank Vision Fund set for new portfolio champion with Coupang IPO 6

By Sam Nussey and Joyce Lee

TOKYO/SEOUL (Reuters) – SoftBank’s $100 billion Vision Fund is poised to have a new number-one asset in its portfolio with the upcoming floatation of top South Korean e-tailer Coupang, furthering a turnaround that has seen the fund yo-yo from huge losses to record profit.

The $50 billion target valuation that Reuters reported this month would likely see the decade-old firm surpass recently listed U.S. food deliverer DoorDash Inc on a roster of assets that also includes stakes in TikTok parent ByteDance and ride-hailers Grab and Didi.

The Vision Fund built up its 37% stake in Coupang for $2.7 billion, mostly at an $8.7 billion post-money valuation, a person familiar with the matter said. The fund is not expected to sell shares in the initial public offering (IPO) that Coupang filed for in New York, the person said, declining to be identified as the information was not public.

SoftBank Group Corp and Coupang declined to comment.

Achieving a $50 billion valuation would add to good news for the fund which is bouncing back from an annual loss in March. This month, it announced record quarterly profit, driven by the listings of DoorDash and home seller Opendoor Technologies Inc and share price rise of ride-hailer Uber Technologies Inc.

HIT PARADE

The fund has written big cheques for late-stage startups to fuel rapid growth, with two-thirds of the value of its portfolio concentrated in 10 assets including Coupang.

The 10 include 25% of British chip designer Arm – to be sold to Nvidia Corp pending regulatory approval – but not stakes in high-profile stumbles like office-sharing firm WeWork.

The fund’s largest assets include its 22% stake in DoorDash, whose share price has doubled since the firm’s December IPO, sending its market capitalisation to $65 billion.

FACTBOX: Vision Fund’s investment hit parade

SoftBank initially invested in Coupang in 2015, adding it to a stable of e-commerce hits that included 25% of China’s Alibaba Group Holding Ltd, before placing it under the fund.

The e-tailer has grown rapidly during stay-home policies while the COVID-19 pandemic has forced other portfolio firms like Indian hotel chain Oyo to scramble to preserve cash.

Analysts see Coupang’s $50 billion valuation as feasible given its first-mover status and as it expands beyond replacing brick-and-mortar retail with a rising number of online channels.

It is the biggest e-tailer in South Korea that directly handles inventory, with 2020 purchases at about 21.7 trillion won ($19.62 billion), showed data from WiseApp.

“The market’s assessment isn’t exaggerated,” said analyst Park Eun-kyung at Samsung Securities. “Coupang’s market leadership is a premium factor.”

($1 = 1,106.1800 won)

(Reporting by Sam Nussey in Tokyo and Joyce Lee in Seoul; Editing by Christopher Cushing)

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