Jamil  Ahmed, Distinguished Engineer at Solace

The energy sector has faced many disruptions and challenges in recent years, from pipeline disruption to the growing demand for hydrogen. However, the most significant of all of these is the global desire to decarbonise. The growing concern over fossil fuels has created intense pressure for businesses to transition towards renewable energy sources and cut carbon emissions. Governing bodies have begun to impose regulations on organisations to force them to cut emissions by 3.4 gigatons of carbon dioxide equivalent (GtCO2e) a year by 2050, which amounts to a 90 per cent reduction in current emissions.

The constant development of markets and digital transformations will only increase the demand for energy in the future across all industries. Therefore, reducing emissions, in reality, is no small feat, however harsh or impressive the targets may be. To make decarbonisation a reality in the near term, businesses must adopt an inward-looking strategy to reduce emissions through their own operations. These are termed Scope 1 emissions and refer to emissions released as a direct result of one’s own current operations. Achieving this requires companies to streamline their operations, and improve their internal visibility to measure and track energy consumption.

Detecting emissions

The major challenge companies face in accurately measuring their energy consumption lies in overcoming the mass amounts of siloed data within their system. These data silos not only diminish productivity but also bury these useful insights, compiled into a mountain of data that is hard to identify and analyse. Ultimately, data silos are a result of organisational infrastructure built for a previous era, one with limited technological adoption, and limited pathways for dataflows. Over time these have created complex organisational barriers.

The lack of data transparency in organisational infrastructure is severely undermining businesses’ ability to gain insight from their existing data. This also impacts their ability to share data with external partners in search of meaningful solutions for decarbonisation. The value of data sharing cannot be overstated when searching for innovative solutions. A recent study shows that 45% of businesses in the energy sector see analytics and innovation as critical tools. With the entire energy sector’s ability to effectively decarbonise hinging on data sharing to drive innovation, gaining greater data insights are non-compensatory.

Another major consideration in decarbonisation is power reliability planning when transitioning to renewable energy sources. Solar and wind energy rely on changeable weather factors for operability, the varying levels of power readiness in these energy sources make them difficult to implement into the national grid. This makes reliably planning this an increasingly complex and important part of the decarbonisation journey as the sector must test for long-term stability and the potential for energy transfers and storage. A solution must be found that can address these real-time concerns.

Reliability in Real-time

Real-time data is the information that is delivered immediately after collation and enables businesses to respond to information at lightning speed. Real-time data has a host of usages in the energy sector, from alerting major weather changes that may impact power reliability to detecting overheating or electrical wastage in appliances. These information transfers are known as an ‘event’ that requires further action or response.

Real-time capabilities play a major role in overcoming data transparency issues associated with the sector, in its ability to connect interactions across systems and processes could enable energy providers to effectively identify opportunities in reducing energy wastage.

Event-driven Decarbonisation

Enter event-driven architecture (EDA), the structure that underpins an organisation’s ability to view event series that occur in their system. EDA decouples the events from the system so that they can be processed and then sent in real-time as a useful information resource. This can then be analysed by resource companies to assist with optimising decarbonisation initiatives.

The strength of EDA is its scalable integration platform, as this allows companies to manage enormous quantities of data traffic coming from multiple data streams and energy sources. From this, energy companies can develop durable systems by aggregating information. This can then be sent to control systems to identify power outages or extreme weather events and conditions.

To achieve this, an architectural layer known as an event mesh is required. An event mesh enables EDA to break down data silos and facilitate the real-time integration of people, processes and systems across geographical boundaries. Implementing an event mesh also upgrades and streamlines existing systems/processes to enable better data transparency in real-time data sharing. It is unsurprising that given the great benefits of EDA both in terms of its scalability, durability and agility that a recent study found 85% of organisations surveyed view EDA as a critical component of their digital transformation efforts.

Decarbonising for the future

Regulations on the energy sector are rapidly increasing, most recently the US Senate passed the Inflation Reduction Act (IRA) on August 6th of this year. This Act signals the intense pressure on the energy sector to immediately undertake significant decarbonisation initiatives. It is designed to accelerate the production of greener and more renewable energy sources such as wind and solar. Once nations like the US have begun higher production of the technology that can harness these energy sources, others will follow suit. The only way the large-scale adoption of renewable energy sources will occur is if businesses build real-time capabilities to become event-driven businesses. Only then can the transition to decarbonisation and achieving net zero become a reality.

By Raj Dasgupta, Director, Global Advisory, BioCatch

The last two years have created a perfect storm for account opening fraud. Many banks and organisations were unprepared to handle an increase in online transactions and the widespread usage of digital services spurred by the pandemic.  Criminals exploited the system by falsely applying online for economic relief packages and then opening bogus accounts to deposit their stolen money into. It has been revealed that account opening fraud in the UK, was at its highest level in more than three years in 2021.

The latest wave may have passed, but there are ripples in the distance. Criminals are opportunistic, and their strategies are continuously evolving.  As highlighted in our recent webinar with the Royal Bank of Canada, it is critical that financial institutions are aware of the latest account opening fraud strategies, finding a balance between decreasing risk and exposure, while providing a great customer experience.

New Strategies for Account Opening Fraud: Combining Human and Non-Human Activity

Account opening fraud enables criminals to carry out money laundering. As we saw with economic relief packages, criminals are targeting where the money is — claiming unemployment or stimulus benefits, for example — and opening accounts to deposit stolen funds. They then move the money out to other accounts, often many times over, or buy cryptocurrency to conceal to make it hard to trace the origin of the funds.

Financial institutions that rely on PII or device-based risk assessment to detect account opening fraud are finding that their controls are falling short. Criminals have clean sets of PII data to work with to make their way through the account opening process, and the problem is so commonplace there are even how-to videos on YouTube to walk would-be criminals through the process. Because of the flurry of activity, banks had to act and began investing in new technology, like machine learning-based models, to shut the door on criminals. However, they have continued to adapt.

Criminals have a new MO and are using bots to open accounts at scale. Criminals leverage automated scripts and large caches of stolen PII to submit new account applications in minutes. Because most banks have bot detection technology in place to detect this activity, criminals have modified their attacks to blend real human interaction or introduced time delays on purpose with the intention of mimicking a human.

It’s now an incredibly sophisticated operation, mixing human activity and non-human programs to attack and confuse financial institutions.

Risks for Anti-Money Laundering and Fraud Teams

Although account opening fraud is a critical component in the money laundering supply chain, there is room for AML and fraud detection teams to work together on the problem.  Mule account detection is a serious challenge for financial institutions, both at account opening and within existing accounts.

In the world of mule accounts, there are criminals that open accounts with false paperwork or with a stolen or synthetic identity. There are also individuals who will sell their genuine account or multiple accounts to a criminal to make fast money. AML teams’ step in to investigate these accounts when there is a trigger, like a large transaction, that is indicative of money laundering. AML investigations can take weeks, months, or years once suspicious activity is uncovered. However, there are opportunities to prevent money from moving out of these accounts at all, and fraud teams can collaborate with AML teams to achieve this goal.

To reduce risk, we need to blur the lines between fraud and AML teams. One way to do this is by using technology that analyses user behaviour to uncover activity that is out of the norm for a genuine user, either at account opening or later in the customer life cycle.

Someone using an account for money laundering may behave like this:

• A customer opens an account and uses it like a regular account for awhile
• A criminal takes over or purchases the account from a genuine user and lays low, leaving the account dormant for a period of time
• Then, suddenly, there is a host of incoming payments followed by outgoing payments

Technology like behavioral biometrics monitors user behaviour over time to detect these patterns, and can flag the accounts for money laundering activity, preventing money transfers from going through.

How to Create an Uninterrupted Account Opening Experience

Despite our best efforts, fraud will never be eradicated. It will change because criminals are flexible. “You have to find a way to balance what is an acceptable level of risk versus a delightful level of experience for the user,” Dasgupta noted.

One way is to layer machine learning and other technologies to “provide that balance between a beautiful user experience with the appropriate level of friction, while at the same time reducing your fraud exposure,” Dasgupta said.

Behavioural biometrics examines user behaviour during account opening to detect signs of illegal conduct. Criminals, for example, frequently employ copy and paste or excessive deletions while filling out a web form. Genuine users know their personal information from long-term memory and thus their typing patterns appear much different than those of a criminal using stolen PII. Because behavioural biometrics also works silently in the background, it does not add friction to the user experience. Instead, the technology identifies tell-tale signs that can build a bigger picture of who’s behind it, how they are behaving, and what is really happening when someone is applying for an account.

There are additional strategies for finding the right balance. First up is choosing controls that pair well with your users and the devices they use. Mobile users are conditioned to provide a second factor, like a thumbprint, but your web banking audience may be less open to extra steps. Second is deciding what transactions are low risk for your organisation and setting priorities for higher value transactions or clients. Financial institutions also shouldn’t cut corners on the measures they have in place to meet compliance requirements.

Banks have to address reputational risk, too. If today’s discerning consumer doesn’t like what an FI does, they can switch apps and go to a competitor.

Banks are vulnerable to account opening fraud, but by stacking smart fraud controls, they may reduce fraud risk while improving customer acquisition and improving the account opening experience.