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BIG DATA IN FINANCE: FROM DESCRIPTIVE TO PRESCRIPTIVE ANALYTICS

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Companies and consumers are both preoccupied with data. Companies want to know how they can make the best use of the data they gather, while customers try to ensure that companies collect as little data about them as possible. Data is hot and, in our technology-driven world, everyone is engaging with it in one way or another. Business customers and consumers are often reluctant to provide data, yet it is in their interest to do so. So it is also important that companies use data to continually enhance the customer experience. At the Onguard Academy, organised by FinTech company Onguard and commercial data supplier Altares Dun & Bradstreet, finance professionals learned more about the possibilities of big data in finance.

 

Old approach in a new guise
“What we see happening today is not as new as you might think!” The Onguard Academy opened with these words from Joris Peters, a Data Scientist at Altares Dun & Bradstreet. He was referring to credit (risk) management. The importance of credit risk management (also known as receivables management) was recognised back in Ancient Greece. Some 2,300 years ago, the Greek philosopher Aristotle is reported to have said: “Creditors have no specific interest in their debtors, but only desire that they may be preserved, such that they may repay.” In other words Aristotle recommended that creditors treat their debtors well, because it increased the likelihood of payment. And this is still the essence of 21st-century credit management.

 

Martin de Heus

However, the way companies organise credit management has changed and this has to do with the times in which we live. New technology and computing power are the main changes. Today, computers and other devices are capable of highly efficient and more sophisticated data capture. They also have far greater storage capacity. They capture and transmit millions of data units daily.

 

Data is everywhere. It can be easily stored and fed into machine-learning algorithms. Ignoring data is no longer an option. This also applies in the world of credit management where we now have so much information about the individual debtor. It’s time we used this knowledge to improve their customer experience.

From descriptive to prescriptive
The use of data is not new. Though it may have gone unnoticed, we have actually been working with data for many years. By combining large sets of (un)structured data from different sources, it is now possible to use data not only as a basis for informed decisions, but also to predict customer and debtor behaviour.

 

This is what we mean when we talk about ‘big data’. The use of big data involves three dimensions of analytics competency. Ultimately, we want to progress to the third dimension. This enables companies to make a real difference. The three dimensions of data analytics competency are:

  1. Descriptive: The volume of available data allows finance professionals to look at the facts, past and present. At this level, the use of big data is pretty straightforward.
  2. Predictive: It is possible to run analytics on historical (descriptive) data and identify payment patterns. These patterns can be used to predict what might happen tomorrow. At this point we are approaching the final dimension.
  3. Prescriptive: The third and most interesting dimension of big data analytics is the prescriptive level. Once you can predict that a debtor will pay late or default, it is wise to take action. You can then preempt potential problems before they occur. Herein lies the promise of the prescriptive dimension of big data analytics.

 

What does this mean for the credit manager?
Big data can make a difference in organisations, especially in finance departments. Almost half (41%) of finance professionals anticipate that, two years from now, their departments will not be able to operate without big data. This was one of the findings of the annual Onguard FinTech Barometer survey.

 

But what does it mean for the credit manager? The use of big data is causing credit managers to wonder about their future in the industry. The FinTech Barometer survey revealed that 21% believe big data will have a significant impact on employment. This is both logical and inevitable. Jobs are always evolving and big data is a game changer in this respect. By enabling companies to anticipate certain risks well in advance, it can be used to preempt potential problems with customer payments, thereby reducing Days Sales Outstanding and increasing cash flow. Big data also gives companies a complete overview of their business processes and their status, so the senior management and the board can make informed decisions about the future of the company.

 

So, will the use of big data erode the role of the credit manager? Well, that depends on how they approach it. Credit managers are facing a new challenge: they have to assume a more strategic role and they also have to be able to analyse the data so they are a valuable addition to the management. This is how your company can make a difference.

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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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