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How real-time data can help lower UK energy bills

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Jamil Ahmed Distinguished engineer, Solace

 

The UK has continued to see surging energy price rises. Following an increase in the UK’s energy price cap, by October, a household’s typical energy bill will be set to rise to a whopping £800. The costs are being passed on to the consumer, already struggling with rising inflation. So what can be done to help reduce a household’s energy bills to help the impact on one’s finances?

The rising price of energy can be attributed to several reasons. Firstly is the initial economic bounce back following the pandemic seeing soaring demand. However global factors such as the windless summers seen in Europe, droughts in South America and the ongoing conflict in Ukraine have all been major contributors to price rises. While the UK government has already offered loans and begun debates for a windfall tax, this is only a short term fix, and more must be done.

 

Making meters smarter

Over the past 10 years, the UK government has made concrete efforts in the installation of smart meters. Despite the initial onset cost of delivery and installation, the government believes that the benefit will be seen in the long term across households in the UK.

Yet smart meters have taken a public beating. The criticism is not the direct fault of the device itself, but rather in its execution. ‘Non-smart’ meters, what we are used to seeing, do not do enough to bring a full context into one’s energy consumption. Typically, when looking at their meter, a consumer will simply see a number rising with no specific understanding as to what it refers to in terms of pound for pound usage. This data adds little value to a consumer and has no impact on the potential positive changes that can be made in everyday energy consumption.

The first step to solving this issue is to improve the consumers’ understanding of how they typically consume energy throughout any given day. This means there needs to be a complete overhaul at both the consumer and company level. If a consumer is more knowledgeable as to how much their energy usage impacts their bills, they will be better informed of how reflective their daily habits of consumption are.

By presenting the data registered by smart meters – for instance through an interactive display, a mobile app or even a simple website – it gives consumers a deeper level of understanding of their energy consumption habits. If a consumer can see the differing prices for energy as part of a smart tariff, they can adjust their behaviour accordingly. This will be used to their advantage, such as moving the usage of their washing machine to the next day, or charging their car late at night if costs are lower in the evening. This is a clear example of nudge theory, a concept in behavioural economics that passes positive reinforcement and indirect suggestions onto the consumer. In this case, not only will it reduce a consumer’s energy bill long-term, but it will allow for further environmental initiatives to be pushed forward.

The question is how can smart meters offer the highest level of accuracy and timely nudges to the consumer? The answer lies in real-time data movement.

 

How the government can act

In 2021, the Office of Gas and Electricity Markets (Ofgem) announced it would create the Market-Wide Half Hourly Settlement (MHHS). This proposal is a key enabler in supporting the transition to net-zero. It will contribute to creating a cost-effective electricity system and encourage more flexibility in one’s usage of energy to lower household bills.

The programme will mandate that all energy companies provide half-hourly updates from providers on a consumer’s energy consumption. When put in place, it will see a boom in the efficiency of smart meters as it allows consumers and businesses alike to reduce energy usage, moving us towards a greener Britain.

For smart meters to provide this level of insight, it will require a seamless movement of vast amounts of data between the smart meters to the energy providers, and back again to the consumer across different channels. As it stands, smart meters summarise the consumption data in, usually, daily updates to the provider. This move to half-hourly will mean a 48x increase in data volume to process from each meter. Existing IT infrastructures are not currently built to cope with this expected massive change in data volume. Energy providers must deal with the issue sooner, rather than later.

 

The key lies in real-time event-driven architecture

Real-time data movement occurs when a piece of information can travel almost immediately the moment an event happens, all across the chain of interested applications and users. Consider the smart meter as a publisher of consumption events, with the energy provider and the members of the household as simultaneous subscribers to those same events. Event-driven architecture (EDA) organises IT systems around the flows of these ‘events’ to provide a more seamless and ‘joined up’ experience across different business functions and services. Companies in the retail sector have adopted such an approach to implement ‘omnichannel’ shopping experiences, for example.

Only when there is no delay in such data movement, can it truly act as the ‘nudge’ to positively adapt and lower usage habits, or detect potential energy wastage. This envisaged decrease in total usage benefits both the consumer’s wallet and the environment.

In fact, it is for this reason that Ofgem has proposed for the MHHS programme to embrace EDA as its underlying IT foundation. The need for real-time has already been recognised by many to drive greater value out of their data. A recent global survey reported 71% see the benefits outweighing the costs of implementing EDA. Thankfully, Ofgem has recognised the need for EDA in lowering prices, helping power the race to net zero.

EDA will be the sole differentiator for MHHS to be successfully implemented and achieve the desired aims. It will allow energy providers to react in real-time to updates from smart meters, and in turn, inform consumers through apps and services in real-time of usage insights with new capabilities such as trend reporting and more accurate future consumption predictions.

Environmentally speaking, the race to net-zero can and should be powered by real-time data. Off the back of MHHS, Ofgem could introduce ‘smart tariffs’ to act as an incentive to decrease energy usage. When prices are lower during certain periods, customers can be made aware, in real-time, through a multitude of channels (such as mobile apps or smart home devices), encouraging them to take advantage of this shift and alter consumption levels. Taking this one step further, it can be applied to connected appliances as well, helping alter usage patterns to optimise costs based on the insights available from this new future. Imagine electric vehicles automatically adjusting charging activity themselves to optimise costs.

Eventually, the proposal of encompassing EDA into the UK energy sector is both a welcomed and necessary one. Event-driven architecture will be the pivotal driver, long term, for reducing costs on both sides of the energy aisle.

Business

Defining Fraud in 2023

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Scott Buchanan, Chief Marketing Officer at Forter

Fraudsters are fluid — they constantly experiment with new tactics to find cracks in a merchant’s defenses. In 2023, there are five trends that merchants need to be aware of — we saw each in 2022 and expect to see them with even more frequency in the year ahead.

Human ‘Bot’ Farms

First, let us acknowledge that while “human bots” is an oxymoron, it is also highly insensitive. At present, our industry lacks a better way of describing the practice. It used to be that human ‘bot’ farms referred to sweatshop-style arrangements in which poorly paid workers, often in developing countries, spent their days on brute force attacks, solving things like CAPTCHAs.

Now, though, a new twist on this old theme has arisen. In short, human bot farms use trafficked humans to scale their fraud operations. Often, they behave as bots, conducting brute force (and similar) attacks.

Human bots were widely recognised in fraud manager communities as a driving force behind recent repeated attacks, especially during the holiday rush. For example, human bot farms bombarded merchants that offer limited edition merchandise, decreasing the chances that prized products find their way to (and ultimately frustrating) good customers. These same operations also applied several tactics that follow at a scale that overwhelmed some fraud solution providers and their merchant customers.

Low-tech Address Manipulation

In the past year, fraudsters reverted to old tricks to circumnavigate rule-based fraud prevention as we saw an uptick in low-tech address manipulation. Consider a merchant with a rules set that checks a shipping or billing address against a negative list. And let’s say a noted fraudster has an address of 123 Main Street that is on that list. Therefore, any transaction with a shipping or billing address of 123 Main Street will be blocked by rules.

Fraudsters found an easy workaround. They simply write a variation of the address during checkout that evades the rules but can be easily understood by FedEx, UPS, or any other delivery company. For example, 123 Main Street becomes One-two-three Main Street or 123 Maain Street.

This should be simple to identify and block in theory. Still, fraud managers were frustrated that rules-based solutions — even those that applied artificial intelligence to speed rules application — struggled to spot this manipulation. During the Black Friday rush, more than one vendor threw up their hands and admitted they had no way to stop this tactic effectively. And as a result, fraud teams with these solutions had to manually review a growing queue of transactions.

Triangulation

With the growing presence of marketplaces to exchange goods, fraudsters are using triangulation more. Think about this as ‘stolen to order’ (instead of made to order). A fraudster posts a sought-after item for sale on a marketplace; in 2022, some of the most popular items for triangulation were high-end ‘cozy’ blankets, sneakers, gaming systems, and other electronics.

When a consumer buys an item from a fraudster on the marketplace, the fraudster then steals the item from a merchant. They input a shipping address for the marketplace buyer at checkout, which typically evades address verification checks. The marketplace buyer gets their item; the fraudster gets their money; the merchant gets penalised, and the marketplace is entirely unaware.

Fraudsters prefer triangulation because they don’t make any effort until they have a buyer — they never have to worry about stealing something they can’t sell, and they never have to touch the merchandise (further reducing their operating costs).

Double-dipping

Emboldened cheaters are attempting more brazen tactics. A prime example of that is double-dipping — while this is not new, we did see more attempts (especially from amateurs and previously good consumers) to double-dip in 2022.

Double dipping can take any form where a bad actor wins twice. For example, the bad actor makes a purchase and has the product shipped. They tell the merchant the item was not received and simultaneously file a chargeback with their issuer. Since it may take hours or days for the issuer to inform the merchant of the refund request, the communication gap can mean the bad actor receives money back from both entities and keeps the product.

We’ve also heard examples of bad actors buying and receiving an item, then filing a return, yet failing to return the item. Instead, they send the merchant back a package with rocks (or something else weighted). In one particularly devious example, a bad actor filled a bag with dry ice, which evaded a weight check by the delivery company, and then arrived at the merchant as an empty package.

Friendly Fraud

The best-known form of friendly fraud is chargeback fraud when a customer makes a purchase and receives it but files a fraud chargeback claiming that the purchase was made by a fraudster. This form of friendly fraud has been growing dramatically in recent years. Less recognised is that other forms of friendly fraud — which can also be labeled policy abuse — are increasingly serious.

For example, a consumer buys a sweater as a final sale. When it arrives at their doorstep, they realise it doesn’t fit as they’d hoped. Disappointed, the (previously good) consumer contacts the merchant to claim the sweater never arrived (code = Item Not Received) and demands a refund. The consumer now has the item they can wear (hey, at least the fit is close) or resell on a marketplace for profit.

Friendly fraud can also surface as returns abuse (returning items worn or outside of store policies), promotions abuse (re-using new customer discounts or other voucher codes), and more.

Friendly fraud is difficult to stop since it is often perpetrated by good consumers — they don’t appear on negative lists or fail basic rules. But professional fraudsters get in on the same acts, industrialising the consumer problem by increasing its scale and professionalism significantly. To increase their odds of success, they have gotten pretty systematic about this form of fraud. For example, on the dark web, fraudsters have shared the exact language to use when calling specific large merchants or issuers to nearly guarantee a refund or chargeback.

Parting Thought: The Power of Identity

The above tactics that fraudsters used with some success in the past year generally exploit gaps in rules-based systems (deployed by the merchant and/or offered by a fraud solution provider). These tactics don’t work when you can pinpoint the identity behind an interaction.

When you can be statistically confident that the identity entering an address of “One-two-three main street” is associated with fraud, it doesn’t matter what they enter in the address field; their transaction attempt is blocked. When a known fraudster is attempting to put an item up for sale on a marketplace or purchase an item with a net new shipping address, you stop them. And when they try to re-use promotional codes repeatedly, you reject the attempt.

You cannot pinpoint an identity with rules — instead, you need a massive graph of online identities and as much data as possible on each. While fraudsters always manipulate aspects of their identities, they cannot mask thousands of data points. Next-generation fraud solutions that use machine learning to augment human expertise can pattern match and pinpoint identity.

And to build the largest identity graph, you need a consortium of the largest merchants — collectively, they will ‘know’ the vast majority of online identities. And in this model, an identity — a bad actor or a good customer — known to one merchant is immediately known to all merchants.

And that is why the final trend for 2023 will be merchants abandoning rules-based systems at an increasing rate. That includes the rules-based fraud solution providers masquerading as machine learning (but really just speed up the application of rules). To combat more sophisticated fraudsters, merchants will make decisions based on identity. They will seek out the largest identity graph in order to achieve superior results.

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Mizuho Bank Luxemburg upgrades anti-financial crime compliance risk management with Napier

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Mizuho Trust and Banking (Luxembourg) S.A , the Luxembourg subsidiary of Japan’s Mizuho Trust & Banking division (part of Mizuho Financial Group) , is upgrading its Transaction Monitoring  framework strategy through a partnership with Napier, the financial crime compliance technology specialist.

An intelligent compliance platform, Napier Continuum, including Transaction Monitoring, Client Screening, Perpetual Client Risk Assessment and Client Activity Review, will provide Mizuho Bank Luxemburg with a holistic overview of compliance that will enable it to connect data, control compliance operations, and manage risk.

The bank wanted to upgrade its framework to make it more robust given the importance of financial crime for credit institutions.

Naim Tliba, Chief Compliance Offer and Vice President at Mizuho Trust and Banking (Luxembourg) S.A., said: “We chose to work with Napier as it has the flexibility to meet our needs, at the same time offering the most advanced technology supported with powerful AI. With improved transaction and client monitoring capabilities, our organisation will be able to stay ahead of the curve and provide our clients with the most secure and regulated asset servicing experience.”

As part of Mizuho Financial Group, Mizuho Trust & Banking (Luxembourg) S.A.  has been formed in 2000 and provides securities and fund services to its institutional clients.

Napier’s compliance technology helps businesses and financial institutions to comply with local and international anti-money laundering (AML) regulations, monitor transactions, and screen customer and business partners and therefore participate to the efforts to better combat financial crime.

Greg Watson, CEO at Napier, said: “Our range of new-breed compliance solutions help organisations like Mizuho Luxembourg to gain control over their risk management so that it can become a competitive advantage. The technology is one side of this, but it’s the capability to adapt a system in adherence with local regulations that offers the most effective solution, and that’s what we have been able to provide Mizuho Luxembourg. Approaching a system upgrade in this proactive way means that they will be equipped with a futureproofed anti-money laundering strategy that will take care of their AML compliance needs.”

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