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Artificial intelligence in banking industry: Conversion To Genuine Benefits

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Artificial intelligence in banking industry Conversion To Genuine Benefits

Today, it seems, hardly a week goes by without mentioning another company which joins the ranks of those, who have already launched their own virtual assistants – from news networks to stores. In the banking sector, traditional call centers are being replaced by AI assistants. However, despite the obviousness of this trend, the introduction of AI technologies may have a comparable financial effect in other fields of application, experts in the field of banking say.

Artificial intelligence may be something fundamentally new for many industries – but not for the banking sector: according to analysts at Accenture, who prepared the Banking Technology Vision 2018 report: Building the Future-Ready Bank, banks could be considered as “veterans” in terms of the history of using these technologies: the first neural networks for automatic lending appeared in this industry more than 20 years ago.

At the same time, the AI is evolving now in an unprecedented way, and the interim results of this transformation are expected to be truly impressive: according to the report, 79% of the banking sector representatives anticipate that in the next two years, technologies will advance to such an extent that artificial intelligence could work in financial organizations equally with living people as an employee and reliable advisor.

Industry experts are already estimating the benefits that large-scale introduction of AI technologies will bring to companies in the sector. For instance, Autonomous Research, a US independent analytical agency that conducts research in the field of financial technologies, offers the following estimate: in the United States alone, artificial intelligence will provide banks and financial institutions with savings of 22% until 2030, or more than $1 trillion.

The components of this impressive figure demonstrate in which areas AI is expected to be the most effective. From the report of Augmented Finance & Machine Intelligence it follows that in the front-office the amount of savings will be about $490 billion – almost half of the aforesaid sum (due to a reduction in the number of specialists in cash opeartions, security personnel, retail banking network and other staff). Another $350 billion will be saved by using the AI in the middle-office (technologies of customer identification and verification (KYC / AML) as well as other forms of data processing). Finally, AI application in the back-office – regulatory and accounting units – will reduce the expenses of banks by another $200 billion.

In the meantime, one can hardly find a country with banking sector smoothly adopting artificial intelligence without facing any structural challenges. Russia is no exception. In November 2018, the Russian rating agency Expert RA in cooperation with the Center for Financial Technologies conducted a study on “Artificial Intelligence in the Banking Sector” with participation of Russian market leaders in the application of AI and machine learning technologies.

The results of the study showed that the use of AI technologies in the Russian banking industry is hampered by the scattered nature of data and information systems. Moreover, analysts say, even after solving the problem, banks are more likely to experience an acute shortage of specialists capable of processing this data.

Sergei Putyatinsky, Deputy Chairman of the Management Board of Credit Bank of Moscow, shared his opinion on the process of introducing AI and machine learning (the bank ranked “above average” in terms of the use of these technologies according to Expert RA): “We are pragmatic in implementing “hype” technologies. The investigation of technology begins with a limited pilot project, which allows evaluating its utility, creating internal competencies. Where it is possible, we use free software. Each project is calculated in terms of recoupment, and only in this case the decision on its implementation is made. Our top-priority directions in adoption of these technologies are: working with full-text documents, making credit decisions, working with overdue debts, financial monitoring, and while doing so we try not to redo existing solutions, but to search for still manual areas and to automate them by using new technologies.”

The problem of training qualified personnel impels banks to enter the educational solutions market more actively: for example, since spring 2018, CBOM has been implementing the IB Universe internship program, offering students and graduates to gain practical experience in investment business (IB) in a number of areas, including information technology.

The goal of such training programs offered by banks seems obvious: in that case heads of departments and specialists can act as mentors, transferring their knowledge and competencies and, in the long run, “raising” new experts in the workplace, reducing the shortage of staff who, among other things, can be “in tune” with a trend set by AI technologies and machine learning.

Of course, the availability of qualified specialists cannot be viewed as an end in itself, nor the funds released through the introduction of AI can be examined from the position of economy for the sake of economy, no matter how huge the amounts might be. It is important to keep in mind that the active use of AI technologies in the coming years potentially may become a decisive argument in the competitive struggle of banks for mass segments not only in Russia, but throughout the world.

Banking

What banks need to know about observability

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What banks need to know about observability 1

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? 2

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 3

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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