Paul Christensen is CEO of fintech Previse
From PPE production to staffing hospitals, the NHS has overcome a myriad of challenges in 2020. The healthcare industry now faces rising Covid cases and Brexit uncertainty while its suppliers experience mounting fiscal pressure. Distributing the Covid vaccine presents the greatest logistical challenge the UK has faced this century. Paul Christensen argues that the healthcare industry and government must take decisive action to secure the whole length of the vaccine supply chain.
If Britain successfully distributes the Covid vaccine, it will constitute the greatest feat the logistics, transport, and health industry will pull off this century. Plans are in place to secure the cold supply chain the Pfizer vaccine requires. St John Ambulance is training thousands of volunteers to administer the vaccine. The first vaccines are already being rolled out to the most vulnerable. However, discussion of the vaccine has neglected one of the most crucial elements of a swift distribution: ensuring accelerated payments for suppliers. Thousands of small firms are expected to play their part in distribution of the vaccine, while having to wait and chase months to get paid.
The face of international supply chains has changed dramatically since the beginning of the pandemic. The British logistics and transport industries have adapted to changing rules, local lockdowns, and shifts across all markets, all while staring down the barrel of either an eleventh-hour Brexit deal or no deal at all.
Regardless of what happens on the first of January, the logistics and transport industries will face new restrictions, which are likely to be overloaded with bureaucracy, all while dealing with the aftershocks of the second lockdown. We are already seeing overcrowding and processing delays at ports. Distributing and administering a vaccine at a national level was never going to be an easy task, but with so many factors to consider, the government and industry needs to do everything in its power to ensure a smooth, resilient supply chain. A key aspect of this is paying suppliers quickly.
Accelerated payment is essential for supplier agility
Traditional finance operations can take up to 120 days to pay invoices. This long-standing culture of long payment terms reduces supplier liquidity and limits opportunities for investment and growth. One of the simplest ways to protect vaccine distribution, avoid bottlenecks and enable supplier agility is to accelerate payment of invoices throughout the whole supply chain.
The problem of slow and late payments isn’t new, even before the pandemic late payments to suppliers amounted to over £23 billion, according to Pay.UK research. The Federation of Small Businesses found that 62% of small businesses have experienced either an increase in late payments and/or had payments frozen completely as a result of COVID-19. The government and NHS cannot afford for late payments or long payment terms to plague Britain’s vaccine supply chain if they want to ensure its successful and safe roll-out to those who need the vaccine most.
Technology enables instant payment
Both the government and the NHS, as well as industry, can adopt Day-1 payment policies for invoices in a way that would also be sustainable long-term for their own cash flow. The technology and processes exist to enable suppliers to access cash immediately without requiring the buyers to change or speed up their payment processes.
Machine learning makes it possible for both the private and public sectors to enable early payments, and benefit. Paying suppliers faster demonstrates a real commitment to sustainability and strengthens supply chains at minimal cost to the taxpayer. Every supplier deserves the opportunity to be paid on day-1.
Patients receiving the vaccine is the final result in a long chain where each step needs to perform flawlessly to ensure the vaccine is delivered. In order to do this, suppliers at each step need to have cash available when they need it to ensure they can perform their part. To create a sustainable vaccine supply chain, we need to take on the inertia that suppliers face when trying to get invoices paid. Increasing the strength of the supply chain as a whole will have a positive impact on every participant and enable a smooth and swift distribution.
From production and transport to the healthcare workers administering the vaccine, the whole supply chain requires fast payment. Now is the time for government, hospitals and industry to work together to provide financial security to the whole supply chain and secure the vaccine’s distribution throughout the UK.
Paul Christensen is CEO of fintech Previse, which uses machine learning to get suppliers’ invoices paid the moment they are received.
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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