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FINTECHS AND BANKING POST-COVID

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COVID-19 has forced businesses and society to adapt to new realities. From big-name Wall Street banks to up-and-coming financial technology start-ups, the pandemic has forced individuals and corporations to look at how business is conducted through a different lens amid ever-changing consumer behaviours. Two years into the pandemic, financial institutions continue to trim their branch footprint and adopt a digital-first approach. The result is a boom in card payments, the expansion of digital channels, and a significant increase in online banking users.

 

The effect on retail banking consumers

Dima Kats, CEO at Clear Junction

US banks and thrifts permanently closed 233 branches in July. According to   US branches as of July 31.    However, even as the financial incentives to do so disappear, banks will need to maintain a physical presence to cater to older customers who may not be online, rural communities, and unbanked individuals; banks are more than a place where people keep money: they can be a vital community hub.

COVID-19 could prove to be a catalyst for the acceleration of the trends we’ve seen towards the increased uptake of digitised services, but it will be some time before changes wrought by the pandemic harden. Some current banking trends — including an affinity to use digital channels and online payment tools — will stick post-pandemic, while others will fizzle out and turn out to be a temporary fix. But people who are new to online and mobile banking channels are finding out that it is easy and quick. Banks will have to make investments to build awareness about options available to customers to make current banking habits stick.

The pandemic has put a spotlight on the importance of digitisation. A traditional bank will always maintain a physical presence, but branches are expensive to operate. Financial technology companies, unburdened by complex legacy systems, are primed to benefit from the shift to digital. A loan from a traditional banking institution can take over a week to complete, but neo banks — with their online-only operations that avoid the costs and complexities of traditional banking — can do that in just a few hours.

Rising infections are accelerating the use of online channels, particularly mobile, to view and manage finances. Fintechs, with their cloud-native approach, are purpose-built for the mobile channel, giving them an edge as a greater number of financial transactions are conducted through digital channels.

Digital payment and e-wallet services are expected to boom in a post-COVID world, as fintechs offer tech solutions in the cyber security space as the pandemic has increased cyber security incidents because of the increase in remote/hybrid working. With new digital finance technology comes new entry points for cyber actors to gain access to confidential data.

Ultimately, human intelligence can only spot and stop a finite number of cyber breaches. Therefore, the implementation and integration of Data Science (DS) applications into established financial systems are crucial for survival in the age of digital payments and e-wallets. Its use can help improve banking cybersecurity by:

  • Automating the threat hunting process to improve cyber threat detection rates.
  • Learning from previous threat patterns and leveraging the information to look for early signs of any potential attack in the future.
  • Predicting cyber breaches before they happen.
  • Securing and flagging potential threats and sending a notification to the IT team to resolve them.

DS already plays an integral role in business operations. Thus, extending it to improve cybersecurity should be a logical and simple transition to execute. However, with new digital finance applications and the increased use of remote devices to share financial data, mobile banking cyber threats are a growing concern. FIs who fail to integrate DS into their cyber security strategy risk financial and reputational devastation.

 

COVID’s impact on artificial intelligence in banking

The Bank of England published survey results in December 2020 which aimed to gauge banks’ appetite for machine learning and DS amid the pandemic. Overall, half of the surveyed banks expected an increase in the importance of machine learning and DS for future operations due to COVID-19.

While banks’ appetite for artificial intelligence doesn’t seem to be waning, the pandemic may prove to be a short-term hurdle as investment capacity is strained. Machine learning is only as good as the data it relies on, but with ultra-low interest rates and weakened revenue, the present is not necessarily indicative of the future. But banks may look to re-train machine-learning models to perform better under adverse economic conditions.

 

Importance of DS in fintech

Financial technology companies use DS-powered solutions to meet the demands of customers who want convenient and safe ways to help with their problems. Help with credit decisions, managing risk, quantitative trading, personalised banking, cybersecurity and fraud detection are just some areas DS is helping fintechs in. Because DS can quickly analyse large quantities of data to deliver important insights and information, it is used to create efficiencies and recognise patterns that help with decision making.

 

Cybersecurity in fintech vs banking

The pandemic has brought about an increase in cyber security incidents as bad cyber actors double down on their attacks through ransomware, malware and social engineering. Hastily implemented cloud data processes and security needs have failed to keep up with technological innovations which may have left financial data exposed. And as digital banking channels are adopted at an increasing pace, fintech users are more at risk than ever. Multi-cloud data storage, AI fraud detection, Secure Access Service Edge networks, blockchain systems and regulatory technologies are among trending innovations that can make fintechs secure and help keep financial data safe amid a rising tide of cybercrimes.

According to a cybersecurity report by Boston Consulting Group, banking and financial institutes are 300 times more likely to be at risk of a cyberattack than other companies. In the current world of remote workers and remotely connected workplaces, cybersecurity is arguably more important than ever before. Un-encrypted data, malware, third-party services that aren’t secure and spoofing are some of the biggest threats to a bank’s cyber security. Much of a bank or financial institution’s operations utilise technology. But without cyber security measures in place, sensitive data could be at risk. These attacks can be countered by using firewalls, multi-factor authentication, biometrics, training, automated solutions and outsourcing.

Companies that are able to predict how changing consumer behaviours will play out after the pandemic will be better positioned to seize opportunities. Understanding these changes can help companies determine what new behaviours are likely to be permanent and which might revert to their pre-COVID patterns. Businesses that can quickly identify and cater for these behaviour shifts will emerge from the crisis better positioned for growth.

 

 

 

Banking

Augmented automated underwriting and the evolution of the life insurance market

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By Alby van Wyk, Chief Commercial Officer at Munich Re Automation Solutions

 

It’s almost inevitable. Spend your working life identifying, analysing, quantifying and ascribing monetary value to risk, and you’re likely to have a fairly strong aversion to it. Or more accurately, an aversion to undertaking new endeavours with inadequately understood consequences. The insurance industry is, on any number of levels, the very definition of risk-averse.

And yet, for all the commentary suggesting otherwise, insurance still has an appetite for innovation. If the insurtech sector is any indication, then an interest in and requirement for new solutions is being recognised and slowly addressed.

Declan O’Neill

It may not employ the language of disruption that runs through the wider fintech market, it may be short a few unicorns and unable to boast some of the record-breaking funding rounds, but a quiet tech evolution has been building in insurance nonetheless. Hence the advent of automated underwriting facilitated by more advanced algorithms and data analysis.

Where insurtech does overlap with its more vocal fintech counterparts is in the greater use of artificial intelligence (AI) and machine learning to solve age-old problems around data analysis and interpretation.

It’s about five years or so since AI first became a topic of conversation in insurance. Since then, despite the intensity of the debate, it has often felt like a reality that is always just over the horizon – a destination that kept moving even as more and more efforts were directed towards it.

But recent research suggests that the journeys made so far have not been in vain. We are at a point where embracement of AI is about to step up a gear. The global value of insurance premiums underwritten by AI have reached an estimated $1.3 billion this year, as stated by Juniper Research; but they are expected to top $20 billion in the next five years. As a destination, it is closer and more attainable than ever before.

However, AI is not an island. Its promise of $2.3 billion in global cost savings to be achieved through greater efficiencies and automation of resource-intensive tasks will not be achieved in isolation.

AI remains part of a more complex ecosystem of data gathering and analysis. It can apply new technologies to get the best out of the already established and still-emerging data sources that feature in underwriting offices around the world. It emphatically does not require these existing investments to be ripped out, replaced or downgraded.

It is more helpful therefore to see AI as the differentiating factor in the latest generation of insurance IT: augmented automated underwriting, or AAU for short.

AAU gives underwriters the ability to spot patterns and connections that are, frankly, either invisible to the human eye or which take normal, human-assisted processes unfeasible amounts of time and resource to identify.

Whereas earlier generations of automation were able to pick up the low-hanging fruit of insurance markets – the individuals whose driving history fit into clearly delineated boxes, for example – AAU can take into account all of the rich complexity of the human experience. It can spot the nuances and individualities that populate the life market, for example, and translate those into accurate policies.

That’s good news for both underwriters and their customers. AAU can significantly reduce the need for separate medicals, repeated questions, lengthy decision-making processes, and drastically increase the speed at which a potential insurer can get a quote and cover – while continually improving the way risk is calculated and managed.

It can make sure the decision-making process remains in the hands of underwriters rather than IT departments, enabling them to set and update the rules and parameters as befits their preferred business model. It consequently makes advanced, complex and precise decision-making available to a broader range of underwriting businesses – which is good for those businesses, good for customers and ultimately good for the entire industry.

AAU – augmented automated underwriting – is an example of the realisation of AI’s promise. As such, it’s set to become one of the key talking points and disruptive technologies of the insurance industry. And this time, AAU is both a journey and destination that all progressive insurance organisations need to be considering for their future operations.

 

 

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Banking

ESG in the finance and banking industry – are you ready?

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By Julian Moffett, CTO BFSI, EDB

 

Environmental, Social and Governance (ESG) has soared towards the top of banking, financial services, and insurance (BFSI) and other boardroom interests. Organisations everywhere know they need to take ESG and greenhouse gas emissions (GHGs) seriously not only because it is the right thing to do for the future of the planet or because it can help attract and retain talent, but also, because failing to do so may pose a risk to the economic value of their businesses and encourage probes by governments, watchdogs and non-execs. However, complying with complex reporting and going the extra mile to actually deliver on the goals of the rules is a challenge in many ways, not the least of which is in achieving the required excellence in data management to underpin strong reporting on ESG.

 

What is ESG? 

Julian Moffett

ESG is an umbrella term that covers a broad gamut of activities. Gartner defines ESG as “…a collection of corporate performance evaluation criteria that assess the robustness of a company’s governance mechanisms and its ability to effectively manage its environmental and social impacts.”

The CFA Institute describes the environmental element as focusing on “the conservation of the natural world” and includes measuring “climate change and carbon emissions,” “air and water pollution” and “biodiversity” among many other measures. Social considers “people and relationships” looking at areas including “customer satisfaction,” and “gender and diversity.” Governance covers “standards for running a company” and analyses factors such as “board composition,” “audit committee structure” and “audit committee structure.”

 

Status of the current regulatory environment

There are many bodies proposing rules to formalise ESG monitoring and seeking to ensure corporate compliance. Some example groups, frameworks and bodies:

  • The Task Force on Climate-related Financial Disclosures (TCFD)
  • Streamlined Energy and Carbon Reporting (SECR)
  • The International Regulatory Strategy Group (ISRG)
  • The Sustainability Finance Disclosure Regulation (SFDR)
  • The International Sustainability Standards Board (ISSB)
  • The Sustainability Accounting Standards Board (SASB)
  • Sustainable Development Goals (SDGs), the Global Reporting Initiative (GRI) support efforts such as the US SEC’s Climate and ESG Task Force.

Financial services organisations are very aware that the current regulatory landscape is far from mature (and will continue changing) both in terms of alignment between bodies and also with regard to when the new rules will come into effect. At the of time of writing:

  • The requirement for Scope 2 disclosures (see below for description) for the Sustainable Finance Disclosure Regulation (SFDR) will likely come into effect in 2023
  • A proposed Corporate Sustainability Reporting Directive (CSRD) should be agreed by the European Parliament this year for implementation in 2024 to report on performance in 2023.
  • Meanwhile, the SEC has just released its proposed rules for climate-related disclosures, which,if passed in legislation, may come into effect as early as year end 2022.

 

Reporting Obligations 

Reporting can cover a wide range of areas covering energy consumption, GHG emissions, water consumption and waste management to health and safety, labour rights, diversity and inclusion to ethical conduct, and even areas such as appropriate executive compensation.

While the regulatory reporting obligations are not yet finalised, the expectation is that compliance may prove to be an onerous task. For example, organisations are under pressure to monitor carbon emissions but even so-called Scope 1 emissions (those that come from owned or controlled emissions) can be hard to track. Factor in Scope 2 (indirect emissions such as purchased power) as well as Scope 3 emissions from up and down value chains, and the reporting task at hand is difficult indeed.

To measure, monitor and manage in addition to staying on the right side of rules, organisations need to have excellent data management fundamentals, strong reporting tools and a new class of applications, which also have the agility to adapt to rapidly changing regulatory demands. Data will be used both to support decarbonisation measures but also to identify where there are disclosure gaps. It was telling that when the SEC issued a press release on its Enforcement Task Force, it specifically referred to data:

“The task force will also coordinate the effective use of Division resources, including through the use of sophisticated data analysis to mine and assess information across registrants, to identify potential violations.”

Having reliable data comply with emerging rules isn’t the only essential requirement for organisations. Institutions need such data to understand where they are in their journey to sustainability, so that they can set sensible targets and track progress against them. Organisations will have to cover the data trifecta of availability, management and transparency. Many organisations may be stuck in the early stages of managing ESG, overly relying on manual processes, spreadsheets and email. But their target should be to get to real-time data insights that are easily visualised, understood and shared. As a foundation, BFSIs need to capture, manage and securely share data reflecting consumption and safety to emissions, financials and data from surveys measuring results against ESG targets. Data emanating from ERP and other back-office systems, performance data from third-party associates, media and social network coverage, spatial/geolocation systems and beyond should also be factored in.

 

Actually reducing GHGs

Organisations are using a wide variety of ways to reduce emissions and improve their footprints from using renewable energy sources to making secondary use of energy; for example, in the case of one university, this is done through capturing data centre heat in hydroponics. For IT, making broader use of multitenancy in cloud computing and hosting services is a popular way to reduce emissions. Not only do these large data centres offer an economy of scale, they also tend to be state of the art in their use of renewables and highly efficient hardware and other infrastructure. Gartner, in an article titled The Data Centre Is Almost Dead, says it expects 80 percent of enterprises will close in-house datacenters by 2025. For me, the jury is out on this one but an interesting one to monitor going forward.

 

Conclusion

We are at the start of a very significant inflection point in regulatory and consumer expectations around ESG. BFSIs should be under no illusion that momentum is building rapidly in terms of having to address strict reporting requirements and implement strategies to reduce GHGs.

However, we also see this as a time of positive change. As the leading provider of Postgres, EDB is excited to help organisations further their ESG goals as the journey unfolds. We are closely monitoring the implications of ESG regulations as they will give rise to a new class of applications and drive adoption of green data centres. We see OSS, including Postgres, as playing a key role in this shift as often the movement to private and public cloud helps accelerate application modernisation and enables displacement of outdated incumbent technology (including database) platforms. As the leading provider of Postgres, EDB is excited to help organisations further their ESG goals as the journey unfolds.

 

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