By: Andromeda Wood, Senior Director of Global XBRL Strategy at Workiva
There are many factors involved in effective financial reporting, which can make it a complex and time-consuming task without the right technology or infrastructure to accelerate it. But it is critical that companies invest this time and resource to deliver an accurate representation of finances, including revenues, expenses, profits, capital, and cash flow, as demanded by stakeholders.
However, business performance reporting is changing. The new proposals for the Corporate Sustainability Reporting Directive (CSRD) will have an impact on internal processes, building on the existing Non-Financial Reporting Directive (NFRD) and will apply to all large companies and all listed companies in the EU. This means an increase from 11,000 businesses who were subject to existing requirements, to nearly 50,000 that will need to follow detailed EU sustainability reporting standards.
Sustainability comes to the fore
Regulation is directing fund and asset managers to ensure that the information and funds are assessed against a common set of criteria (for example, EU sustainable finance, in particular the EU Taxonomy and associated Sustainable Finance Disclosure Regulation). These changes echo the mood of investors both big and small, who are starting to scrutinise where their money is going, as ethics and moral values move up priority lists.
As a result, the decision of whether a business can secure funding is increasingly dependent on how a company performs against environmental, social and corporate governance (ESG) factors. Companies who fail to demonstrate the right metrics (or progress towards them), may face challenges accessing capital.
CSRD comes to the forefront
Conversations around social movements and increasing concerns around climate change, inequality, and diversity have left the investor community questioning the impact of their decisions. Investors want to be sure that their investment is aligned with their vision for the future. Indeed, in a recent survey conducted by Workiva, 66% of all respondents in Germany wanted to know whether a company lived up to their social and moral beliefs before investing.
As a result, it’s clear that the existing NFRD no longer met the changing needs of modern investors. The CSRD was proposed to enhance and strengthen the measures already in place, following three years of consultations, market pressure and political discussion. The greatest shift this new directive brings is an emphasis that the term ‘non-financial’ is technically incorrect. It’s now apparent that sustainability factors have a financial impact on the business and should be considered alongside finances as part of the organisation’s Annual Report.
Financial reporting no longer just in the office of the CFO
The increasing focus on ESG data brings operational change, encompassing a wide range of factors which may reside across different departments outside of finance, such as human resources, supply chain, and business development. Information from across the company will be required, both to align operations with a sustainability strategy and to fulfil disclosure requirements in areas such as carbon footprint and employee demographics.
To address this, a clear end-to-end reporting process that is well-managed, produced in the right format and remains compliant is necessary.
Companies will be required to digitally tag reported information (powered by Inline XBRL) to ensure that it is machine-readable, standardised and comparable against other organisations. Importantly, such improvements must not compromise the ability; indeed, XBRL will speed up the aggregation of data for users. Finally, an assurance obligation with respect to the sustainability disclosures, ensuring audit professionals will be required to confirm the validity and accuracy of reviewed information.
Internal siloes are barriers to success
Responding to the new CSRD first requires a frank assessment of the processes currently in place and the areas in need of attention to bring the whole function up to optimal performance. As financial reporting tends to be a complex, yet well-oiled machine, companies can leverage the experience they already have to help bring the ESG data and controls in alignment.
In doing so, the company will have oversight of all operations; and joint reporting will break down the walls between finance and sustainability. This will ultimately help streamline processes.
The right technology will augment existing models. Automation, for example, will accurately and efficiently collect and integrate data from different departments across the business. From this, the data is analysed to determine the materiality, before being collated into the right, easily audited, format.
Processes fit for a brighter future
Emphasis on ESG issues will only continue to intensify and investors want to be sure that the companies they invest in are genuinely committed to sustainable practices, and unlikely to have a detrimental impact on the planet and society. Large investors already see the importance of valuing a company based on a broader set of standards, as these are linked to current and future performance.
Compromising integrity, breaking regulations, or having heavy fines implemented as a result of issues ESG aims to address will fundamentally devalue a company. The new regulations – and investor demands – will force organisations to be more transparent with their reporting. This would allow potential investors to have a clear understanding of their practices and intentions, enabling them to make more confident, informed decisions on where to invest money. It’s critical for organisations to get their houses in order, by streamlining processes now to prevent losing out on future investments.
AIRBANK SELECTS YAPILY TO BUILD A FINANCIAL MANAGEMENT SOLUTION FOR SMBS
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
AI AND HOW IT’S LEADING THE FIGHT AGAINST FRAUD IN THE FINANCIAL SECTOR
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.
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.
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
- Large data sets (TeraBytes, PetaBytes) consisting of both internal company data supplemented with third-party data;
- Highly optimised and validated AI/ML algorithms that detect fraud and minimise false positives and false negatives;
- 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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