Financial recovery post-pandemic: how technology can help banks to help their customers.
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Financial recovery post-pandemic: how technology can help banks to help their customers.
The Bank of England reported in October that UK banks are resilient enough to continue supporting households through the post-pandemic recovery. They are thus in a good place to help those customers in financial difficulty (in particular, the one in ten UK adults who anticipate having to borrow money to cover essential costs, and the 1.6 million people who were still on furlough by mid-August).?
Banks will be key to the financial recovery of the households and businesses rocked by Covid-19. But can they carry the weight of the public’s financial worries?
If banks are to withstand the demands made on them by customers in times of financial challenges, it is vital that they invest in tech driven solutions now. Technology can address the key areas of customer experience, data optimisation, costs to serve, and security, risk and compliance.?
A strong technological footing will enable banking providers to focus on their human response to customers, all whilst unlocking incredible value for the business. Think of it as this – tech can support banks, so that banks can support the public.
Record breaking investment in UK fintechs of almost ￡18bn in August this year should also ring alarm bells for traditional banking providers. But the latter do have distinct advantages over fintechs, which put them in a position of real power- reputation, credibility, trust, and a lot more financial security. Their foundation is strong, and simply needs to be built upon.?
Frictionless convenience, immediacy and intimacy without contravening perceived privacy boundaries are now everyday demands for the modern consumer. Exploiting AI and associated technologies will be the difference between success and failure in the coming years. According to McKinsey, AI can potentially unlock $1 trillion of incremental value for banks – but what should they be building, and how can it help banks to become more human in their approach to customers?
Banks represent some of the organisations with the most data – both internal and external – and yet many traditional banking businesses are struggling to fully leverage it.?
Data and analytics can help banks to make swifter lending decisions, set more accurate risk exposure limits for customers, tailor offers to individuals, and more accurately market specific products to particular customers.?
For those customers requiring loan products, payment holidays and credit cards, data can help banks to better understand customers and their preferences and habits. This information can then be used to build better, more intelligent methods of serving and retaining that particular cohort – in a way that also protects the banks themselves from great risk.?
In order to achieve this, banks must have a smart data and analytics strategy that scales and spans across the business. They should strive towards mass personalisation and a single view of the customer across all products – including lending, savings and insurance.?
AI, Machine learning and Robotic Process Automation (RPA) can efficiently process customer demands
Since the start of the pandemic, millions of people in the UK have been granted payment holidays (which incidentally, end later this month). In addition, almost nine million people have been on furlough, and more than 800,000 people have lost their jobs.
With branches closed and life confined to the four walls of our homes, it’s easy to imagine that customer service teams for big banks would have found themselves inundated with queries and requests from worried customers during the height of the pandemic.
AI, RPA and machine learning are revolutionising finance – and chatbots in particular are a great example of how machine learning can be used to increase productivity and improve customer experience by automating 24/7 customer support. The application of these technologies can also improve retention, increase product sales and drive top line growth.?
With chatbots, customers can get quick answers to their queries. Machine learning can be trained to identify which of those queries can be swiftly solved with RPA, and which require support from an employee – that all important human touch.?
In order to truly enhance the customer experience, banks need much more than a standard, rules based chatbot. Conversational AI harnesses Natural language processing (NLP) and ML to understand the intent of customers, predict what they might want, and even predict and determine the mood of a customer.?
Robotic process automation (RPA) can also be used to assist with processing repetitive tasks such as customer onboarding and account opening. RPA makes the process easier and more straightforward – enabling customers to access much-needed financial services quickly, and saving banks time in what is traditionally a long, drawn-out process.?
Predictive algorithms can inform and improve the customer experience
Historical data can be used to predict future events and trends – benefiting both the bank and the customer. For example, predictive analytics helps banks to identify and segregate ‘risky’ customers from risk-free customers – and target specific, affordable products appropriately.?
Algorithms can also improve the customer experience by offering budgeting support – helping people to avoid late overdraft payments and other penalties. For those customers experiencing financial worries, this is an incredibly valuable part of the customer service experience, and would help embed trust in the banking provider.
Customised loans can help banks to make the best decision for the customer
One of the toughest challenges for banks lies in predicting the level of debt that is affordable for each customer. AI systems trained on credit decision data can help banks to make the best decisions for both consumers and the business – helping to prevent customers from taking on unaffordable loan products.??
But AI is only as good as the data that feeds it. AI systems must be vigorously tested, as, like humans, the algorithms that determine credit decisions can be subject to bias. This bias can have a negative material (or even harmful) impact on people.?
The right training and testing is vital, as AI systems can be fed on biased credit decision data that ultimately prevents groups of customers from accessing certain financial products. It is therefore vital to mitigate bias in AI systems to build trust between humans and machines.?
Other tech solutions worthy of mentioning here also, include moving operations to the cloud in order to reduce cost; blockchain; and virtual and augmented reality (such as a virtual digital bank branches and digital wallets).?
With banks playing a crucial role in the financial recovery of both households and businesses post-pandemic, reinventing them for the future with optimal infusion of new-age technology is a prime necessity. Tech, ultimately, can help banks to help their customers.?
However, this should not come at the cost of the all-important human touch. Tech should act as an enabler, promoting meaningful relationships and interactions rather than replacing them. The option to speak to a human should always remain – particularly given the reality of financial vulnerability across society. By striking a balance between an empathetic emotional approach and technology-led innovation, banks can deliver a sustainable and profitable customer experience that makes lives better.?
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DON'T MISSCybersecurity: The Crucial Double Check
TECHNOLOGYAutomation key to making compliance easy as regulatory challenges prevailAutomation key to making compliance easy as regulatory challenges prevail 3
Automation key to making compliance easy as regulatory challenges prevail 4By Elizabeth Williams, Senior Director at Puppet
Financial services (FSI) organisations are confronted by more cyber security challenges today than ever before. Over the past year, regulatory bodies have responded to a proliferation of cyberattacks with new laws under Australia’s updated Cyber Security Strategy 2020 and the Essential Eight strategies.
The proposed changes to Australia’s critical infrastructure bill will allow intervention in major attacks to an expanded list of essential services, including financial services. Businesses in these sectors are required to improve baseline security for critical infrastructure to ensure products and services are protected from cyberattacks.
The threat of regulatory intervention is very real. The recent NSW’s Auditor-General report found that none of NSW’s lead government agencies have not reached even level one maturity for at least three of the Essential Eight strategies, effectively failing to improve cyber security safeguards.
Apart from heavy fines and the potential loss of banking licenses, FSI organisations have a duty to maintain trust and integrity in the financial system and support the national agenda to enhance cyber resiliency and security.
Addressing various compliance needs
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As regulatory standards shift upwards, maintaining compliance to pass audits and to maintain costs has become more complex. IT leaders need to enhance their technology security posture and remain aligned to the Australian Signals Directorate (ASD) and APRA’s guidance, including achieving the right maturity level in the implementation of the Essential Eight.
This is in addition to, and conflicts with the growing demands on product development and innovations, addressing quickly changing customer expectations. Finding the balance between both requires many pieces that must coalesce to create a holistic solution.
A big part of the problem lies in how most security and ITOps teams still work in silos with disparate tools and priorities. The inconsistency leads to increased spending and duplicated work, visibility gaps between teams, and creates more challenges in the painstaking and time-consuming processes to pass audits.
Infrastructure as code is becoming the leading approach in FSI’s environments to drive efficiencies and increase flexibility.
Automation platforms allow teams to manage compliance without disrupting, or duplicating, the security team’s workflow. Having visibility into infrastructure changes as they happen and homing in on the types of changes that could be malicious enables the operations team to work more closely with the security team to provide a clear view of what’s happening. Tools that provide a holistic view of compliance status throughout cloud and on-prem environments can generate automatically updated reports that depict the current state of the infrastructure and can be easily interpreted without deep technical knowledge.
Importantly, it helps IT teams follow a consistent, reliable process for each stage of the compliance lifecycle — from assessment to remediation to enforcement – and gain confidence in their compliance posture.
Automate compliance without impacting agility?
FSI IT leaders that incorporate continuous compliance policies into their infrastructure can save thousands of dollars and countless hours by reducing the complexities and overhead of audits.
Gartner found that by 2023, 60% of organisations in regulated verticals will have integrated continuous compliance automation into their DevOps toolchains, improving their lead time by at least 20%.
Puppet recently worked with DBS, one of Asia’s leading financial services groups to enhance overall security and efficiency through automation of its security configuration management. The security configuration definitions set by international organisations were converted into an automated capability to scan servers in the bank for the purpose of non-compliance reporting and rectification. With the automation, DBS was able to reduce the equivalent effort of 13 staff down to three while freeing up the time and energy for engineers to invest in other value-driven innovation or projects that the organisation could benefit from in the long term.
Closer to home, ANZ Bank rapidly enforced compliance across operating systems with the 22 regulatory bodies. By partnering with Puppet to redirect engineer hours from audit explanations, the bank was able to improve its scalability and enforce consistency across platforms.
The challenge will remain in the foreseeable future for the sector to meet strict rules and regulatory requirements, from strengthening cybersecurity governance, controls including vulnerability remediation and everything in between. Failing to maintain compliance can put the organisation at risk of everything from lost business to substantial fines.
By encouraging operations and security teams to better leverage scalable and intelligent platforms, FSI organisations can drive better collaboration and ensure they comply with the most rigorous security requirements without compromising on agility.
Microsoft optimizes training for large-scale AI models.
The Lead
[1] Microsoft’s Tutel optimizes a mixture of experts model training?
[2] Demystifying zero trust security?
[3] Do culture and employee retention matter? Report reveals surprising answer
The Follow
[1] Microsoft this week announced Tutel, a library to support the development of mixture of experts (MoE) models — a type of large-scale AI model. Tutel, which is open source and has been integrated into fairseq, one of Facebook’s toolkits in PyTorch, is designed to enable developers across AI disciplines to “execute MoE more easily and efficiently,” a statement from Microsoft explained.
MoE models are made up of small clusters of “neurons” that are active only under special, specific circumstances. Lower “layers” of the MoE model extract features and experts are called upon to evaluate those features. For example, MoEs can be used to create a translation system, with each expert cluster learning to handle a separate part of speech or special grammatical rule.
Compared with other model architectures, MoEs have distinct advantages. They can respond to circumstances with specialization, allowing the model to display a greater range of behaviors. The experts can receive a mix of data, and when the model is in operation, only a few experts are active — even a huge model needs only a small amount of processing power.
In fact, MoE is one of the few approaches demonstrated to scale to more than a trillion parameters, paving the way for models capable of powering computer vision, speech recognition, natural language processing, and machine translation systems, among others.?>> Read more.
[2] Nvidia sees how vulnerable its enterprise customers’ datacenters are, leading it to fast-track its zero-trust platform to close growing cybersecurity gaps.
Many enterprise datacenters rely on decades-old security infrastructure that stops at the perimeter. For bad actors and cybercriminals, this is the equivalent of leaving datacenters’ doors unlocked. As a result, Nvidia sees datacenter attack risks grow in complexity, speed, and severity to customer’s operations, combined with a need to support AI and data science workloads.
The latest series of announcements at Nvidia’s GTC 2021 event earlier this month reflects the urgency Nvidia has to harden datacenter security and support customers’ AI, machine learning, and data science workloads at scale.
Nvidia is succeeding at its mission of demystifying zero trust in datacenters, starting with its BlueField DPU architecture. Its architecture includes secure boot with hardware root-of-trust, secure firmware updates, and Cerberus compliant with more enhancements to support the build-out of its zero-trust framework. One of Nvidia’s core strengths is its ability to extend and scale DPU core features with SDKs and related software, while scaling to support larger AI and data science workloads. >> Read more.
[3] New research from NTT DATA reveals only 16% of organizations rank employee retention and engagement as a top priority, and the majority feel unprepared for continued business disruption. Amid the Great Resignation, many organizations are struggling to retain and attract top talent. However, customer satisfaction and financial performance remain the primary focus over employee engagement, even though employees have a direct impact on business outcomes.
The research also provides insights into innovation and digital technologies, again highlighting a disconnect between the importance of the workforce and organizational priorities. Nearly half of respondents say they believe employee skills are detrimental or have no impact on their innovation efforts, but organizational culture is noted as the top reason holding them back from investment in digital technologies. Although nearly half of respondents report a positive culture and management, only 5% claim employees factor into strategic and operational decision-making.
The research polled 1,000 business and IT executives across 16 industries to uncover how organizations are prioritizing digital investments and strategies. >> Read more.
The Funding Breakdown
Simpro lands $350M - Simpro, a field service management software company based in Brisbane, Australia, claims to offer a solution in software that eases the burden on field workers and their managers. The company’s platform provides quoting, job costing, scheduling, and invoicing tools in addition to capabilities for reporting, billing, testing assets, and planning preventative maintenance. The company’s CEO Sean Diljore says the money will be put toward product development and customer support, with a particular focus on global trade and construction industries.
Verbit secures $250M - The startup, developing an AI-powered transcription platform, is now valued at $2 billion. Verbit plans to expand its workforce while supporting product research and development as well as customer acquisition efforts. Beyond this, CEO Tom Livne said that Verbit will pursue further mergers and acquisitions and “provide enhanced value” to its media, education, corporate, legal, and government clients. Verbit’s voice transcription and captioning services aren’t novel —?but its adaptive speech recognition tech can generate transcriptions that the company claims achieve higher accuracy than its rivals like Cisco, Otter, Voicera, Microsoft, Amazon, and Google.
Deliverr raises $240M - Deliverr, an ecommerce fulfillment startup headquartered in San Francisco, California, reports it will use the capital from its series E to grow its ecommerce fulfillment network. Using predictive analytics and machine learning, Deliverr anticipates the demand for products based on demographics, geography, and other variables. The platform then uses the analysis to “pre-position” items close to areas of demand, stocking items across a network of over 80 warehouses, cross-docks, and sort centers. Deliverr is now valued at $2 billion post-money.
Vercel gets $150M - The San Francisco-based company offers a web app development platform based on the open source frontend framework Next.js. Its new funding will be put toward hiring, R&D, and customer acquisition, according to Rauch. This now values the company at $2.5 billion post-money. Vercel’s mission is to provide the estimated 11 million JavaScript developers in the world with the workflow to develop Jamstack sites. With Jamstack, the app logic typically resides on the client-side without being tightly coupled to the backend server.
Payhawk nabs $112M - London-based company Payhawk, an integrated financial software platform that combines corporate cards, bill payments, expenses management, and more in a single system, plans to use the fresh capital injection to prepare for its U.S. launch next summer, and to launch in Australia, Canada, and Singapore by the end of 2022.
Rescale raises $55M - San Francisco-based Rescale, a startup developing compute infrastructure for scientific research simulations, reports that the new funding will be put toward expanding its platform, service offerings, and workforce. Whether they leverage compute from Rescale’s infrastructure or from a third-party provider, Rescale customers gain access to software that supports simulation for aerospace, automotive, oil and gas, life sciences, electronics, academia, and machine learning.
AI learns to manage its own data?
The Lead
[1] AI will soon oversee its own data management?
[2] Cloud security shifting to ‘dev’ not ‘ops,’ Snyk says?
[3] Why siloed SaaS tools do not play well with others.
The Follow
[1] AI thrives on data. The more data it can access, and the more accurate and contextual that data is, the better the results will be.
The problem? Data volumes currently being generated by the global digital footprint are so vast that it would take literally millions, if not billions, of data scientists to crunch it all — and it still wouldn’t happen fast enough to make a meaningful impact on AI-driven processes. So, many organizations are turning to AI to help scrub the data that is needed by AI to function properly.
According to Dell’s 2021 Global Data Protection Index, the average enterprise now manages 10 times more data compared to five years ago. With data being generated in the datacenter, the cloud, the edge, and on connected devices around the world, expect this upward trend to continue well into the future.
So going forward, the question isn’t whether to integrate AI into data management solutions, but how.
AI brings unique capabilities to each step of the data management process, not just by virtue of its capability to sift through massive volumes looking for salient bits and bytes, but by the way it can adapt to changing environments and shifting data flows. AI can automate key functions like matching, tagging, joining, and annotating. From there, it’s adept at checking data quality and improving integrity before scanning volumes to identify trends and patterns that otherwise would go unnoticed. All of this is particularly useful when the data is unstructured. >> Read more.
[2] Developer security platform provider?Snyk brings an approach to cloud security that makes the company stand out from others in the space. There are signs the market will increasingly move in the direction of the fast-growing company, according to Guy Podjarny, cofounder and president of Snyk.?
In an email statement to VentureBeat, Podjarny responded to a question about the emergence of a new category in cloud security — the cloud-native application protection platform, or CNAPP. A CNAPP offering brings together numerous tools, including tools for securing cloud infrastructure, cloud identities and permissions, virtual machines, containers, and serverless functionality.
Vendors and analysts have touted this unification of tools as a benefit for businesses, since it reduces the complexity involved in securing cloud environments and applications. Some CNAPP vendors are now also offering tools for the proactive identification of vulnerabilities during app development — an area where Snyk has been a pioneer and a leading player.
?
[3] In Slack channels and Zoom calls across Silicon Valley, few buzzwords have more cache at the moment than “customer-centricity.” Tech companies are obsessed with delivering better experiences for the customer, reasoning that personalized, reliable services will deliver exceptional business returns over time. Rather than developing products and working to build excitement for those products among customers, this new approach relies on the voice of the customer to drive development.
But while these large tech companies pay lip service to the idea of the exceptional customer experience, they have so far refused to address one of the biggest pain points for enterprise tech customers: a vast number of siloed SaaS tools that don’t play well with one another. While each solution plays an important role in the customer’s tech stack, the majority of developer teams are left to build cumbersome, clunky workarounds to accommodate tools that aren’t interoperable.
Siloed systems and proprietary software cause headaches for the end user both during everyday use and in moments of crisis. When you can’t connect your systems, it’s more challenging to understand what is happening throughout your tech stack and how one function affects another. In particular, it’s much more difficult to fix things when they break — imagine trying to fix a car without having visibility into how fluids flow from one part to another. The end result of this situation is the exact opposite of the stated goal of customer centricity: the user experience is slow and frustrating, with everyday tasks demanding an outsized amount of time and attention.?