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Cyberattacks: 2023’s Greatest Risk to Financial Services  

Miguel Traquina, Chief Information Officer at iProov 
Miguel Traquina, Chief Information  Officer at iProov

New year, same big problem. Without doubt, cyberattacks have posed and continue to pose the single biggest threat to the UK’s financial services industry

by Miguel Traquina, Chief Information Officer at iProov 

Three in four industry execs in the UK deem a cyberattack to be their highest risk factor and, as the economy enters choppier waters, this threat is rising, with those expecting a high-impact cyberattack in the next three years rising by 26% in the second half of 2022 versus the first.  

2022 has been another year of seismic change in the cybercrime space. Types of attacks are evolving rapidly, and consumer awareness is growing. Now, more than ever, we’re starting to see huge end-user demand for greater online protection from identity theft and other online threats.  

Public and private sector organisations around the world are responding by exto increase digital trust and enables with the goal of increasing digital trust and enabling their customers to prove they are who they claim to be securely and easily.  

The pace of advancements in digital identity verification will only accelerate more in the coming year, especially in a high-value and highly sensitive industry like financial services, with more innovation and regulation on the horizon. As we welcome 2023, here are my top four predictions for the year ahead.  

Biometrics + device will overtake password + device for 2FA  

Calling out the ineffectiveness of passwords as an authentication method isn’t new, but what will be new next year is that finally this stubborn, outdated mode of authentication will be overtaken by the use of biometrics in twThroughout-factor (2FA and MFA) use cases.  

Over the course of 2023, password + device will be replaced by biometric + device. 

The uptake of MFA has been steadily rising in recent years, especially since the enactment of PSD2 for electronic payment services in Europe. While passwords are technically compliant as a strong authentication factor, they and other knowledge-based techniques leave a lot to be desired when it comes to security and user-friendliness. Biometrics and other inherence-based security hit the perfect balance between providing the necessary protection to make 2FA and MFA truly secure while also delivering an effortless user experience.  

Liveness checks become mandatory for online identity verification in financial services 

Speaking of regulation, 2023 will also see the European Banking Authority mandate all regulated financial service providers in the EU complete biometric liveness checks when remotely enrolling customers. These new guidelines will help ease new account of theft, and money laundering. What we’ll also see is consumers feeling more comfortable with, and demanding more, biometric verification at other points of their user journey.   

As this becomes mandatory for financial services in Europe, attackers will turn their attention elsewhere – which will require the UK and other regions to follow suit. 

Synthetic identity fraud will break records 

Synthetic identity fraud exploded in many regions in 2022, even becoming its own industry. That is set to continue in 2023, with Aite Group estimating $2.43bn of losses from synthetic identity fraud this year. Nearly every organisation is at risk of onboarding a fake person and the implications that come with that: financial loss, data theft, regulatory penalties, and more. Organisations throughout the financial services world will need to ramp up their online security to identify synthetic identity crime attacks. 

Deepfakes become ubiquitous as the next generation of digital attacks 

The technology to create convincing deepfakes is now so readily available that even the novice cyberattacker can do serious damage.  

Any financial services organisation that isn’t protecting its systems against deepfakes will need to do so as a matter of urgency. More sophisticated bad actors have already moved on to advanced methods, and in 2023 we’ll see a proliferation of face swaps and 3-D deepfakes being used to find security vulnerabilities and bypass the protocols of organisations around the world. 

 Privacy-enhancing government-backed digital identity programs will pick up pace – and they’ll be interoperable 

Consumers globally are realising they don’t want to give their addresses and other personal data to every website or car rental firm or door-person outside a bar. As demand for secure identity services grows, more state and federal governments will begin to roll out interoperable digital ID programs that use verifiable credentials to enable citizens to cryptographically confirm details about themselves. 

Device spoofing will grow exponentially  

The increase in reliance on devices as a security factor has attracted the attention of cybercriminals, who are exploiting vulnerabilities for theft and other harm. In 2023, we will see an increase in the sophistication of criminals spoofing metadata to conceal their attract top made to appear like a mobile device) to circumvent enterprise security protocols. In 2023, organizations – especially those that rely on mobile web – will recognize the limitations of once-trusted device data and move verification services to the cloud. 

CategoriesAnalytics Cybersecurity IBSi Blogs RegTech

Identity Verification for FinTechs: Ensuring Security and Compliance

Vivek Sridhar, Neokred
Vivek Sridhar, Chief Business Officer at Neokred

For Neo banks in the financial industry, digital onboarding is becoming more crucial. Neo banking is the name given to a new breed of digital-only banks that provide a broad variety of financial services via online and mobile platforms.

By – Vivek Sridhar, Chief Business Officer at Neokred

These financial institutions frequently build on top of the already-existing infrastructure, and they significantly rely on technology to give customers a smooth and effective experience. The procedure for signing up for and creating a new account with a neo bank is known as digital onboarding. It is a crucial part of the customer experience and has the power to build or break a person’s relationship with a new bank.

For modern banks, identity verification is a vital step in the customer onboarding procedure. Since it serves as the first point of interaction between the bank and the customer, digital onboarding is crucial for neo banks. It establishes the tone for the customer’s entire banking experience. A quick and easy digital onboarding procedure can provide consumers with a good first impression and persuade them to keep using the bank’s services. On the other hand, a lengthy and onerous onboarding procedure can deter clients from joining up or even cause them to give up completely.

Digital onboarding is essential for neo banks because it enables them to gather vital data about their clients, such as their details, income, and financial objectives. Initially, it is vital to prevent fraud and safeguard the bank and its clients from financial losses. Identity verification is the first line of security against attacks when criminals try to open phoney accounts using stolen identities.

Second, regulations seek identification verification. Anti-money laundering (AML) and know-your-customer (KYC) guidelines oblige financial institutions to verify their customers’ identities. Compliance with these standards is crucial if you want to avoid large fines and reputational harm.

Furthermore, identity verification is key for neobanks because it allows them to collect critical information about the consumer, such as personal information, income, and financial goals. A major use of this data is for offering specialized financial products and services.

Who Needs Technology for Identity Verification?

Financial institutions are a popular target for criminals attempting to conceal the proceeds of their illegal activities, Insurance companies, gaming organizations, and cryptocurrency dealers are just a few of the other industries that run the risk of moving money from and to online accounts.

Large amounts of personal data are transferred, processed, and stored by healthcare organisations. As a result, they are a prime target for cybercriminals looking for this valuable data and may also consider using identity verification software to protect their business and customers.

Given the harmful effect, any association with money laundering and financial crime can have on an institution, groups that engage with customers online rather than in person require a KYC plan to protect their clients, build trust and protect their business from fraud and data breaches.

As part of the onboarding process, these organisations must identify and verify users. But it does not end there. They must continuously repeat the process throughout the customer relationship to ensure that they do not pose any risk to the organisation at any time. The verification process should not impede providing an excellent customer experience, but rather should efficiently and securely connect a user’s physical and digital identities.

Identity verification software will be of interest to the teams and individuals responsible for designing, deploying, and managing the efforts required to protect the organisation from the risk of financial crime.

How to Find the Best Identity Verification Software in 3 Easy Steps:

Identity verification is critical for ensuring that the financial institution only deals with legitimate customers and follows compliance regulations. When selecting identity verification software for business, several factors must be considered to ensure that the organization’s decision is the best one.

Step 1: Analyze the Requirements

The decision must also be motivated by the specific needs of the business. The industry, customer profile, nature of online engagements, and user experience all impacts the role of identity verification as well as its correct function.

Step 2: Gauging the Features and Functionality

With a definite knowledge of the necessities for identity verification software, the emphasis moved to what providers choose to offer. Some features are critical to a solution and knowing what they are and how they are presented are critical to deciding on it with knowledge.

Step 3: Gauging Fit

As suggested solutions are considered, the choice of the safest alternative for the organisation should remain focused on meeting the needs of the business. Although there may be cost savings, some solutions require the vendor or in-house engineers to modify systems and do not give the team the flexibility to tailor the solution to the organization’s need

Using Neokred’s ProfileX Product by organizations to eliminate fraud. Organizations that use ProfileX automate the validation, screening, and decision-making processes required to approve good customers faster, stay compliant and reduce the risk of fraud.

AML teams can manage identity and document verification, including non-documentary verifications (name, address, DOB, SSN), watchlist screening, and monitoring using independent and reliable data sources — scanning against different lists and databases to validate identity and checking against known or suspected criminals to defend against fraud with better data.

The no-code flag and review platform provided by ProfileX enables teams to create workflows tailored to their specific use cases. These include synthetic checks that use spoofed or falsified personal information to identify entities.

CategoriesAnalytics Cybersecurity IBSi Blogs IBSi Flagship Offerings

Chargeback fraud is growing – can AI and Big Data stem the tide?

Monica Eaton, Founder of Chargebacks911
Monica Eaton, Founder of Chargebacks911

According to our research, 60% of all chargeback claims will be fraudulent in 2023. This means not just that merchants have to consider that chargebacks claims are more likely to be fraudulent than legitimate, but that individual merchants and the anti-fraud industry need to lay the groundwork to collect and analyze data that will show them what fraud looks like in real-time.

By Monica Eaton, Founder of Chargebacks911

While many industries are benefiting from so-called ‘big data’ – the automated collection and analysis of very large amounts of information – chargebacks face a problem. The information that is given to merchants concerning their chargeback claims tends to be very limited, being based on response codes from card schemes (‘Reason 30: Services Not Provided or Merchandise Not Received’), meaning that merchants would have to do a great deal of manual work to reconcile the information that the card schemes supply with the information that they have on hand.

While Visa’s Order Insight, Mastercard’s Consumer Clarity, and the use of chargeback alerts have reduced the number of chargebacks, merchants still have very little data on chargeback attempts. This article will look at how merchants can improve the level of data they receive on chargebacks and how they can use this data to create actionable insights on how to improve their handling of chargebacks.

What is big data?

2023’s big tech story is undoubtedly AI – specifically generative AI. Big data refers to the large and complex data sets that are generated by various sources, including social media, internet searches, sensors, and mobile devices. The data is typically so large and complex that it cannot be processed and analyzed using traditional data processing methods.

In recent years, big data has become a crucial tool for businesses and organizations looking to gain insights into customer behavior, improve decision-making, and enhance operational efficiency. To process and analyze big data, companies are increasingly turning to advanced technologies like artificial intelligence (AI) and machine learning.

One example of a company that is using big data to drive innovation is ChatGPT, a large language model trained by OpenAI. ChatGPT uses big data to learn and understand language patterns, enabling it to engage in natural language conversations with users. To train ChatGPT, OpenAI used a large and diverse data set of text, including books, websites, and social media posts. The data set included over 40 gigabytes of text, which was processed using advanced machine-learning algorithms to create a language model with over 175 billion parameters.

By using big data to train ChatGPT, OpenAI was able to create a language model that is more accurate and effective at understanding and generating responses than previous models. This has enabled ChatGPT to be used in a wide range of applications, including customer service chatbots, language translation services, and virtual assistants. Currently, technology very similar to ChatGPT is being used by Bing to replace traditional web searches, with mixed results, but, like self-driving cars, it is a matter of ‘when’, not ‘if’ this technology will become widespread.

AI and fraud

Chargeback fraud is a growing problem for businesses of all sizes. The National Retail Federation estimates that retailers lose $50 billion annually to fraud, with chargeback fraud making up a significant portion of that total. With the significant rise of online shopping, this type of fraud has become even more prevalent, as it is much easier for fraudsters to make purchases using stolen credit card information, forcing victims of fraud to then dispute the charges with their credit card issuer.

Chargeback fraud occurs when a customer disputes a valid charge made on their credit card, claiming that they did not make the purchase or that the merchandise they received was not as described. If the dispute is upheld, the merchant is forced to refund the money to the customer, along with any associated costs, and is typically charged a penalty fee by their payment processor. This not only results in a financial loss for the merchant but can also damage their reputation and lead to increased scrutiny from payment processors.

Where can machine-learning technology help with fraud? To understand this, we have to first understand its limitations. ChatGPT and Large Language Models (LLMs) like it are not Artificial General Intelligence (AGI) – the sci-fi trope of a thinking computer like HAL 9000. Although they can pass the Turing Test, they do so not by thinking about the given information and answering accordingly, but by matching what looks like an appropriate answer from existing text.

This means that while they can produce perfect text by copying existing text rather than ‘thinking’ about the substance of the question, they are prone to producing errors. This is something that isn’t acceptable when it comes to fields like fraud prevention – nonsense answers with a veneer of truth won’t work in the binary world of whether a particular transaction was fraudulent and unfounded accusations of fraud can damage a merchant’s reputation.

What is needed then are AI solutions built specifically for chargebacks. Companies like Chargebacks911 have been working on this for years now, and their solutions are based on big data models that have been built up over that time. Because of their extensive experience working in that field, they are the ideal partner to work with to bring AI up to speed and address the problem of chargebacks.

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