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HOW TO BOOST IT SECURITY AND PREVENT CYBER-ATTACKS IN FINANCE

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Rashid Ali, Enterprise Solutions Manager, WALLIX

 

Cyber-attacks continue to dominate the headlines, and this combined with the fact that financial organisations store some of the most sensitive information in the world, makes the industry a highly lucrative target. All organisations understand the need to have strong cybersecurity measures in place to protect personal and corporate data. However financial services, in particular, have an increased need for advanced security with both money and personal data at risk.

However, despite this, warning research has revealed that approximately 70% of financial institutions have experienced a cyber-attack since the pandemic began. In addition, an Accenture study found that the cost of cyberattacks experienced by the financial services are considerably higher than other industries, reaching up to $18.5 million annually per company – compared to the average cost of $13 million per company when looking across all other industries. Alongside financial implications, cyber breaches also threaten banks and other financial services with potentially disastrous reputational and customer loyalty losses. Trust is crucial in the financial space, and customers want to know that both their assets and personal information is secure. It has never been more important for financial institutions to protect themselves – so how can this be achieved in the face of ever-evolving cyber criminals?

Rashid Ali

Privileged accounts and users are at the heart of the financial services industry – they are needed in order to grant different permission levels and enable employees to do their day-to-day role, handling sensitive information in a secure and compliant setting. In order to truly defend organisations from risks associated with these accounts, it is essential to implement a robust privileged access management (PAM) solution.

 

Combating against ever evolving cyber attacks

The challenge facing many financial institutions is that they are storing more and more private information while at the same time embracing the new digital age. Access to online banking, the ability to transfer money at the touch of a button and instant approval on loans and other services is a major competitive differentiator – and something consumers expect. But, from a security viewpoint this also means more information for cyber criminals and an ever-expanding attack surface. This opens up more vulnerabilities and potentially weak entry points.

Many traditional banks and other institutions also tend to have intricate and complex infrastructures. Many of them have been transitioning their old legacy infrastructure while trying to keep up with the rapid digital transformation that is happening in the financial industry. Within a single organisation there could be hundreds of applications used by thousands of employees across numerous locations.

As part of the very nature of their operation, financial services also tend to have a multi-layered approach when it comes to operations and security, which presents a unique set of challenges. They often share information and work with external providers that use privileged accounts to complete their work. This combined with the fact that the industry is complex with strict regulatory requirements means that the potential damage of a breach is much more alarming. Defending against all these risks can seem highly complicated.

Although privileged accounts and privileged users increase the attack surface, they are required to keep financial services, workflows and processes running smoothly, so this is where institutions need to focus their security. If not managed correctly, hackers can easily get hold of root privileges and make radical changes that can lead to serious consequences.

 

The role of privileged access management

The good news is that a strong privileged access platform combined with strong endpoint protection enables financial institutions to overcome these challenges, while providing the digital services and third-party access they need. In addition, these platforms also help enable compliance through strong password protection, establishing a zero-trust policy and monitoring and recording of privileged access.

Alongside improved security and compliance, privileged access management and endpoint protection also provides added benefits such as quick and easy authentication. This removes a long drawn-out process that hinders employee and customer experience.

PAM reinforces access security through password management, ensuring compliance with the latest regulations, and providing the required information to auditors. PAM also imposes policies that restrict privileged users from bypassing security systems. It secures privileged accounts and allows financial organisations to proactively protect themselves.

Controlling privileged access limits the moves a hacker can make after they have established a foothold within a network. This greatly reduces their ability to move laterally and access sensitive systems. Financial organisations maintain complete control over all privileged users with complete logs and access details, and all actions taken during a privileged session.

With the threat landscape constantly changing, PAM provides the ideal platform that will allow financial organisations to continue the innovation and embrace new services to stay ahead of the competition – while at the same time keeping security and compliance front of mind.

 

Finance

AIRBANK SELECTS YAPILY TO BUILD A FINANCIAL MANAGEMENT SOLUTION FOR SMBS

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Airbank, a financial management solution for European startups and SMBs, has selected open banking infrastructure provider Yapily to help its users manage their finances with ease.

Airbank provides a simple financial management solution that aggregates all bank accounts in one place and delivers more control, visibility, and automation to modern finance teams. Startups & SMBs use Airbank to access bank accounts, monitor cash flow in real-time, create reliable forecasts, and make business payments.

Airbank matches bank transactions with merchant and category data to give finance teams complete visibility into revenues and expenses, thus helping make their lives easier with cash flow budgeting, forecasting, and reporting.

Yapily’s API infrastructure provides Airbank users with a smooth, simple way to connect to more than 1,500 banks across the UK and Europe including Deutsche Bank, Commerzbank, Sparkassen, Volksbanken and neobanks. Airbank selected Yapily for its strong coverage in Europe, with a specific focus on Germany, France, Spain, and the UK. Yapily’s European bank connectivity enables Airbank’s customers to scale and grow across Europe, delivering forecast visibility anywhere they go.

The partnership with Yapily alleviates Airbank’s customers from spending time and resources managing their finances – giving them direct access to all the financial and contextual data they need in one tool. Historically, most businesses created budgets and cash flow forecasts in manual spreadsheets which is time-consuming and error-prone. With Airbank, customers save time and costs to focus on value-adding business tasks.

The partnership also enables Airbank’s customers to use its data enrichment platform and transaction categorisation engine to turn the raw data from bank accounts into meaningful and actionable insights. Airbank reconciles account balances, forecasts financials and helps business owners make smarter business decisions every day. Harnessing Yapily’s leading open banking infrastructure, Airbank can accelerate its adoption of digital banking services.

Airbank’s vision is to simplify financial management for SMBs and to create a unified platform that helps its users with the full cycle of financial management from cash flow analysis and forecasting, to accounts receivables and payables management, and more. Airbank has raised $3m seed funding from leading VCs, and counts hundreds of users in Germany, Austria, France, Spain and the UK.

Open Banking has enabled smooth integrations with banks, which we utilize to offer richer banking and payments experiences for our users. We’re building a business banking solution that connects all your financial accounts in one place. Our partnership with Yapily gives users a smooth and simple way to connect to thousands of banks in Europe, unlocking real-time insights into their cash flow. We eliminate the pains of finance admin so business owners can focus on what’s really important — growing their business.

Christopher Zemina, Co-founder and CEO of Airbank

Airbank helps simplify the daily routine of banking and finance management for small and medium sized businesses. By leveraging Yapily’s open banking infrastructure, Airbank can provide actionable insights to businesses – at a time where it’s needed. As a small yet fast growing company, Yapily is committed to supporting the SMB community and we are excited to see how Airbank delivers the benefits of open banking to many businesses across Europe.

Comment by Chris Scheuermann, Commercial Lead DACH at Yapily

 

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AI AND HOW IT’S LEADING THE FIGHT AGAINST FRAUD IN THE FINANCIAL SECTOR

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Geoff Clark, Managing Director, Aerospike EMEA

Much like many other sectors financial institutions have accelerated their digital transformation projects since the beginning of the pandemic. Lockdown meant that customers could no longer visit local branches or meet in person with their financial advisor. Financial institutions have no choice but to find alternative ways to serve their customers.

We saw banks quickly adapt and improve their automation tools to interact with their customers online.  Technologies that enable chatbots, credit card brokerage, contactless payment cards, digital verification for onboarding, online insurance applications, mobile apps, recommendation engines, robo-investing and robotic process automation (RPA) were just some of the many solutions deployed. Here in Europe, Ernst and Young (E&Y) reported an increase of 72% increase in the use of FinTech apps since the start of COVID-19.

Geoff Clark

Cybercriminals typically opt for the lowest hanging fruit and as financial institutions clambered to expand their digital services the cybercriminals looked to identify and exploit any weakness in the infrastructure providing the backbone for these technologies. Exploiting the vulnerabilities of financial institutions is not new as they have long been a coveted target for fraudsters. In the main, that’s due to the wealth of sensitive personal and financial information they hold. Throw into the mix pandemic relief funds, increased unemployment benefits, and stimulus payments, and you have the perfect playground for fraudsters.

A recent report found that every dollar lost to fraud costs financial service companies as much as $3.78 — an increase from $3.25 in 2019. But fraud’s impact is much deeper than financial loss. It drains company resources to investigate and prosecute fraud, damages reputations, and puts customer retention at risk. For these reasons alone, it is imperative that the appropriate systems and processes are in place to combat fraud.

 

Analysing Fraud

The majority of financial institutions still rely on dated rule-based systems to mitigate fraud risk. These systems can consist of thousands of predefined rules that store, sort, and manipulate data to find fraud patterns. For example, a rule could say, if there is a credit card transaction in one state and another transaction in a different state within a 30-minute time frame, then this is likely a fraudulent transaction and therefore it declines the transaction.

Rule-based systems are static, hard-coded, and time-consuming to update, and are often one step behind the sophisticated techniques fraudsters use. When fraud occurs, the typical response is to create another rule that prevents another attack, but it’s often too late.

Fraudsters continue to find new ways to commit fraud that rules don’t capture.

The trend we’re seeing from financial institutions is to replace rule-based systems with AI and machine learning-based systems as they’re more effective. These systems are largely self-learning and there is so much more data available and the more information they’re fed the more effective they can be. Rather than using tens of data attributes with rule-based systems, AI and machine learning-based systems can analyse hundreds of data attributes over enormous data sets and longer time frames to automatically detect with higher accuracy unusual behaviours that indicate fraud. For example, Barclays Bank has implemented AI systems to detect and mitigate fraud improving the customer experience in the process through the reduction of false positives and false negatives.

AI and machine learning-based systems are heading toward explainable AI (XAI), an emerging sector in machine learning that addresses how AI systems arrive at their black-box decisions. Financial institutions know the inputs and outputs of these systems, but they lack visibility into how they reached the results.

Building XAI into AI systems enables banks to understand how decisions are made and create better models to improve their systems by removing bias. For example, suppose a fraud system declines a legitimate customer’s credit card transaction. In this situation the financial institution needs to understand why the false positive has occurred so it can further refine its model.

XAI also has data privacy in its favour particularly when it comes to compliance. Under the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA)—and with other data privacy laws coming—financial institutions need to comply with specific mandates. They must be able to explain how they use a customer’s personal information and how they came to decision such as declining a credit card transaction. Overlaying XAI on top of their AI systems, ensures they have far great visibility into how decisions are being made by AI/ML systems.

 

Constructing a Fraud System Architecture

To emulate some of the industry’s more innovative organisations financial institutions must understand and pursue best practices when building their AI-based fraud systems. They should work alongside technology organisations but also work with their line of business managers to understand how fraud is impacting their business, what their greatest weaknesses are, how customer satisfaction can be improved, and how they can incorporate customer fraud/risk metrics into their customer analytics to improve their omnichannel marketing campaigns. Customer data collected and analysed by fraud teams are some of the most robust depositories of customer information making them invaluable to marketers.

When looking to build a world-class system, financial services firms should consider the following steps:

  • The fraud system needs to likely consume hundreds of terabytes of data, perhaps even petabytes for the largest firms.
  • Data must be continuously updated in real time from many sources such as internal customer and transaction data from storefronts, web pages, and mobile devices, as well as third-party demographic, behavioural, geo-location, identity management, credit bureau, and other data types.
  • This data will usually need to be prepared, e.g., cleansed, standardised, and normalised, to convert it into a form that AI/ML models can more easily digest and understand.
  • The data needs to move back to the central data platform to be further enriched.
  • At this point those financial institutions can fine-tune the model parameters, test and select the optimal machine learning algorithms, feed them with data to learn the underlying patterns, and validate the model’s accuracy to make good decisions using data that was not part of the training set.

After the above steps are completed and they are satisfied the model can be deployed to act in the microsecond moments that are necessary to fight fraud.

As technology evolves at such a fast pace all organisations must aim to implement a fraud solution that can combat the increasingly sophisticated fraudsters while implementing the following key elements

  1. Large data sets (TeraBytes, PetaBytes) consisting of both internal company data supplemented with third-party data;
  2. Highly optimised and validated AI/ML algorithms that detect fraud and minimise false positives and false negatives;
  3. A real-time data platform capable of running these AI/ML algorithms across enormous data sets in sub-millisecond response times to provide customers with the fast customer experience that they expect.

 

 

 

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