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How Innovative Technologies are Transforming the Banking and Financial Industry

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How Innovative Technologies are Transforming the Banking and Financial Industry
Ashwini Dave, Digital Marketing Expert at Acquire

Ashwini Dave, Digital Marketing Expert at Acquire

The banking and financial services industry has changed dramatically over the years. About half a century ago, consumers regularly walked into a bank branch to withdraw or transfer funds, seek information on financial products, or get their issues resolved. The evolution of ATM brought about the first transformation in the banking industry, making 24-hr financial transactions possible. The arrival of the internet led to further developments in the industry, opening up multiple channels of engagement between consumers and their banks. At the same time, the physical aspect of banking started losing ground, with banks such Santander slashing their branch network by almost a fifth!

The reason?

According to data, customers are less likely to set foot in a branch, preferring to use their smartphone or computer instead. Santander reported a 23% decrease in branch transactions and a 99% increase in digital transactions over three years before deciding to cut down on physical branches and putting more focus on digital.

The Age of the ‘Amazon’

Big players like Amazon have made instant gratification the norm for customers, who expect the same level of service from all their service providers. Therefore, to remain competitive, the banking industry must rapidly embrace the latest technologies to create a frictionless, omnichannel experience for their customers. We are already experiencing one of the most useful customer communication tool, Chatbot.

Consider payments, one of the most basic applications of digital technology in the financial industry.

The transformation in this field was spearheaded by the evolution of physical instruments like credit cards and debit cards. However, physical cards are gradually losing favor, as most people have now moved on to mobile wallets and contactless payment options.

The ‘Data’ Game

How Innovative Technologies are Transforming the Banking and Financial Industry 7

According to the PwC Global FinTech Survey 2017, the financial industry is heavily investing in data analytics to meet the evolving needs of its customers. The Worldwide Semiannual Big Data and Analytics Spending Guide from International Data Corporation (IDC) indicates a CAGR of 13.2% in big data analytics revenue between 2018-2022. By 2022, IDC expects the worldwide BDA revenue to be $274.3 billion. According to the report, the banking industry is one of the largest investors in big data and analytics solutions.

How Innovative Technologies are Transforming the Banking and Financial Industry 8

It’s clear that the banking industry has recognized the importance of the customer data stored on its servers. And, it is willing to invest in technology to exploit this data for personalized marketing and strengthening the security of their systems.

Know Your Customers Better

The financial industry sits on a treasure trove of data collected across various touchpoints, both physical and digital, over the years. And, just like various other industries, banks can use this data to know their customers better.

Take the example of American Express; with a “database of over 100 million credit cards globally, that account for over $1 trillion in charge volume every year.” The banking giant decided to make use of this data and created a sophisticated predictive model to prevent customer churn. By analyzing historical transactions and 115 other variables to forecast potential churn, Amex can identify accounts that are most likely to close within the next few months and take preventive action to keep these customers from leaving.

Big Data is also useful for personalized marketing or targeting customers based on their buying habits. Here, banking and other financial firms can collect data from the social media profiles of their customers and use sentiment analysis to understand their financial capacity and requirements before marketing suitable products to them.

Big Data and Automation

According to McKinsey, up to one-third of all the work in banks can be automated through technology, leading to reduced costs and elimination of human error to a large extent. JP Morgan Chase & Co. have built a data-based automation platform, also known as COIN. Employing a powerful machine learning algorithm, powered by a private cloud network, JP Morgan is using this platform to review complicated documents. Reportedly, the platform only takes a few seconds to complete regular tasks that previously required about 360,000 hours of work, without any errors.

Safety First

Various banks rely on big data and AI to identify fraud and prevent any potential scams. For example, banks can process large amounts of data to identify behavior patterns to reveal any potential risks among their own employees. They can also use data analytics to pinpoint fraudulent behavior and reduce the financial risks associated with digital transactions.

Immersive Technologies in Banking

Nowadays, customers no longer visit banks. They prefer to conduct most financial transactions from the comfort of their homes. However, certain situations demand expert help, or the customers may find it too tedious to fill out lengthy forms, such as claim forms, on their own. In such cases, advanced technologies such as co-browsing and screen sharing can help by enabling real-time connections.

Customers and agents can connect online through secure co-browsing sessions, where the agents can view the current resources of the customers while the customers can hide their confidential data like credit card details and other stuff.

But this is just the tip of the iceberg.

With 5G on its way, immersive technologies are becoming commonplace, and adoption of innovative technologies, such as augmented reality (AR) by the financial sector will lead to quick service and accuracy than ever before.

But the use of AR in banking is not new. Many banks and financial institutions have leveraged the power of augmented reality to curate new banking experiences for their clients.

In 2016, Mastercard partnered with Wearality to create an experience wherein users could buy golf clothes and accessories virtually, without doing anything in the real world!

In Oman, the National Bank of Oman offers an AR-enabled app to help its customers locate their nearest branch or ATM. The app also enables customers to uncover deals and offers as they walk around Oman, and uses their smartphone camera to combine real-life surroundings with an AR projection.

In India, Axis Bank launched the Near Me app, which enables users to search for nearby ATMs, finance-approved properties, and even dining offers in their areas!

But customers still want more. 

As we already know, customers are no longer interested in visiting brick and mortar establishments for banking. Yet, branch banking is far from obsolete. According to research, “in areas featuring a branch, asset holdings are 2.5 times higher than unbranched areas.”

Yet, the majority of banks experience 90% of their interactions digitally. A logical step in the right direction could be the use of augmented reality and virtual reality to create virtual and hybrid branches for autonomous at-home banking.

Such technology will also be beneficial in offering high-quality services to clients in remote areas that lack qualified banking professionals.

How Innovative Technologies are Transforming the Banking and Financial Industry 9

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Artificial Intelligence and Banking

Modern-day customers prefer to interact with their bank online or via mobile. However, most of these conversations are one-way. The customers are provided with a finite set of options that may or may not solve their problem.

So, if a customer wants to do something other than checking their balance or transferring funds or obtaining their transaction history, the chances are that he or she would have to contact the helpdesk for information. However, as contact centers are often overburdened, the customer may have to wait for a resolution. Often, call center agents may also transfer calls to other departments, leading to frustration in customers.

Enter AI, or more specifically, AI-enabled intelligent chatbots that communicate with customers through voice and text-based interfaces, enabling two-way digital communications.

In case you are wondering how an AI-enabled chatbot is different from a regular texting app, the answer lies in NLP that empowers a chatbot to comprehend natural language statements and respond to them intelligently.

Generally, chatbots in the financial sector can answer questions relating to the expenditure of certain items, the location of nearby ATMs or branches, enable instant transfers, suggest well-performing hedge funds, and even match customers with appropriate insurance products like the chatbot introduced by HDFC Life in India.

Take the example of Wells Fargo’s chatbot that uses AI to respond to natural language messages from users on Facebook Messenger. After registration, users can ask the chatbot questions such as how much they spent on food or travel the previous week, their most recent transactions, account balance, etc.

In addition to facilitating instant communication, chatbots save you money by enabling a high degree of automation in the customer services department. According to a study, 29% of customer service positions can be automated through chatbots, leading to $23 billion in savings for US businesses.

Chatbots can also be used to deliver pro-active suggestions to customers based on their transaction history or profile.

AI and Predictive Banking

Did you ever wish you owned a crystal ball that could give you a clear view of your finances?

With predictive banking, your wish may soon be granted.

Predictive banking is one of the most exciting trends in the financial industry. Today, banks finally have access to highly sophisticated technology to make use of the rich data in their coffers and give their customers tailored advice for the future.

With predictive analytics, banks can offer not only customer-specific financial advice but also make accurate customer risk assessments before approving loans and conduct behavior analysis to prevent frauds. Predictive analytics are also employed in the derivatives market to analyze the sentiments of different financial markets to make accurate predictions that help users make informed decisions.

Security Compliance in Digital Banking

Most banks are overhauling their traditional fraud detection systems with AI-based ones, as AI-enabled anomaly detection makes it easier and more cost-effective for banks to detect fraud.

In anomaly detection, the machine learning model is trained to sense the contents of banking transactions for standard patterns derived from historical data. Any deviations from this baseline behavior are notified to a human monitor who may accept or reject the alert. The behavior of the monitor further trains the model to understand whether the deviation was a fraud or an acceptable deviation.

Anomaly detection can also be used to analyze spending behavior, which allows the model to recognize any fraudulent shopping details. For example, a sizeable online transaction from a customer’s account that has a history of small purchases can be instantly recognized.

Banks may also employ predictive behavior analysis to identify potential threats amongst their own employees to prevent any scams or frauds.

In addition to payment frauds, banks are also privy to sensitive customer information or PII and must ensure data safety at any cost. Consequently, it is vital to employ secure e-forms for collecting user data that encode PII so that no other party can see the banking details. Developers can use differential privacy, which is “a mathematical approach that means AI models trained on user data can’t encode personally identifiable information.”

Conclusion

In the present times, every financial institution around the world is leveraging modern technologies such as cloud computing, data analytics, AI, machine learning, etc. to deliver customer-centric solutions. However, rapidly emerging technologies and evolving customer expectations mean that businesses in the banking and financial industry must continue innovating at break-neck speeds, lest they become obsolete.

In short, following trends is no longer going to work for financial institutions who wish to dominate the market. To truly capture the hearts and minds of their customers and secure their business, banks must tap into immersive technologies to create highly personalized customer experiences. They also need to make banking more convenient and intuitive by introducing two-way digital conversations and taking their services to customers’ homes through virtual and hybrid branches via augmented reality apps.

 

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Banking

What banks need to know about observability

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What banks need to know about observability 10

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? 11

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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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 12

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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