By Greg Michaelson, VP, General Manager of Banking, DataRobot
Banks have been using machine learning longer than pretty much anyone else, and for good reason. Identifying the lowest risk borrowers, pricing loans, and forecasting losses all require the use of predictive models. Sometimes these models are simple scorecards. Other times, they are highly complex machine learning models. Many banks have at least some of these core models built, with a process in place for monitoring and maintaining them, but outside the handful of core models, the need for embedding artificial intelligence (AI) and machine learning at a deeper level is an unfulfilled need at most banks.
At least part of this deficiency in AI and machine learning can be explained by the capacity of data scientists and availability of data. Traditionally, data science teams have had their hands full building and maintaining the core models of the bank. Add to that the challenges involved with managing, cataloging, and assembling data, along with the obstacles associated with implementing these models into production, and it’s no wonder that progress has been so slow.
To complicate matters, the number of players in the banking sector has increased dramatically in the last several years. Fintech companies are hard at work gobbling up bits of market share, and they’re doing it by means of innovation and AI. Banks still have the advantage in terms of data and expertise, but that advantage won’t last if bankers don’t start innovating.
When it comes to AI, some of the lowest hanging fruit in banks today are the so-called relationship businesses. These are typically high-touch, high-profit, complex relationships that have dedicated relationship managers and generate a mountain of revenue for banks. Business banking and corporate banking businesses along with wealth management teams are good examples of this type of relationship business.
Several years ago, I was at a sale offsite for a Fortune 100 company. The attendees were relationship managers that were all compensated on sales. The first half of the day was set aside for discussing prospecting strategies. Salesperson after salesperson stood up and told about the newest event that they had hosted that was really “working” for them. Box seats at sporting events, breakfast meetings, cold calling, and many other in-person events were touted as “the way” that was going to produce results.
At the time, I remember being struck by how unscientific it was. I even made a comment about collecting data to see what was actually working. I was quickly reprimanded: “This is a relationship business,” one experienced salesman told me.
I’ve heard that same sentiment many times since then. “This loan is too complex for a pricing model.” “This relationship is too unique to be lumped into a dataset and learned from.” “This account is much too important to be driven by your models.”
Fast forward a few years, and these relationship-driven businesses are beginning to change their tune. Businesses, who are impacted by ultra-low interest rates and increased competition, are now open to exploring new strategies to grow and deepen their relationships. The opportunity to do so is huge.
The use cases
All sales teams use CRM tools to track leads, opportunities, and accounts. That means that all sales teams have incredibly valuable data that tracks where they’ve been successful and where they haven’t. This data is the foundation of building an AI solution to grow a business.
Prospecting for the best new customers
In the world of sales, prospect lists abound, driven by inbound leads, cold calls, and third-party data sources. Sorting through it all to know who to contact, and who to focus on, can be bewildering — particularly if you don’t have access to any reporting or analysis showing what has been effective (and what hasn’t).
Building an AI solution for prospecting involves first identifying what a “good” prospect looks like. The most obvious definition of a good prospect is anyone that is likely to buy what you’re selling, but that’s far from the only example. Bankers might want to try to predict the credit quality of prospects based on publicly available data to identify the clients where they want to spend their energy. Or perhaps they might want to predict which clients are likely to produce the most value for their business, either by growing in the future or by their needs for a wide variety of products.
In any case, once a good customer is defined, then it’s just a matter of matching up external prospect data with a bank’s existing client base to build a training dataset. For example, I might take the data I have available for my prospects (my features) and match them up to my current clients where I know the target value; e.g., risk rating or profitability (my prediction target). I might try to predict profitability, product appetite, or margin. Once the models are built, I can then score my list of prospects in order to rank them for potential sales efforts.
Deepening relationships with your current customers
The number of products and services that a customer needs is strongly correlated with how profitable that customer is. AI can improve the effectiveness of relationship managers by identifying particular customers that are in need of particular products. If a relationship manager can reach out with a specific product to the right customer (and not reach out with the wrong products), then that customer will be happier, sales will be higher, and the relationship will grow stronger.
Fortunately, if the bank utilises a CRM system, the data needed for this project has probably already been captured. It’s a simple matter of identifying which customers were offered which products and whether or not they bought them. Which customer and product attributes will be predictive of purchasing likelihood will depend on the product.
Once I have my models in place, it’s relatively straightforward to identify the best prospects for a marketing campaign or to have an offer in the “back-pocket” for every customer interaction.
Pitfalls to avoid
These are just a couple of examples of ways that AI can increase the effectiveness of sales teams. Building the solutions is not complex from a technical perspective and deploying them is also pretty straightforward. That said there are a few blockers that you will want to avoid as you build these solutions.
Don’t fail to record information about lost sales. Salespeople hate data entry (doesn’t everyone?). They especially hate entering data about deals that they’ve lost. Capturing data about lost deals, though, is just as important as capturing data about won deals. Machine learning requires data that the machine can learn from. That means positive and negative examples. In order to build models to predict customer behavior, all the possible outcomes have to be captured.
Don’t wait until your data is perfect. Everyone has data issues, but it’s pretty rare to come across a data set-up that is so bad that nothing can be done. Starting with the data that’s currently available is the only way to get started with AI. Waiting until everything is perfect means never getting started at all.
Don’t expect it to work perfectly the first time. These solutions, even though straightforward, take some iteration. I worked with a sales team at a large bank to predict which clients were most likely to need a foreign exchange (FX) product to lock in conversion rates for cross-border payments. It took us six iterations to get to a good model, but when we finally did, we increased the conversion rate from 2% to 10%, which meant a $10M+ increase in sales for the organisation.
Prospecting and deepening models are two ways most banks can start exploring how AI can impact a your organisation. These use cases are low risk, quick to build, easy to implement, and have a high ROI, and advanced tools like automated machine learning now make developing these solutions accessible without the huge upfront cost required in the past; e.g., massive time investment, hiring and retaining large data science teams, tricky manual deployments, etc. Whether you’re a large bank or a credit union, with sizeable complex deals or simple term loans, AI can provide a straightforward way to be more targeted in your sales efforts.
What banks need to know about observability
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Hackers can now empty out ATMs remotely – what can banks do to stop this?
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.
SoftBank Vision Fund set for new portfolio champion with Coupang IPO
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.
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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