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WHAT IS NEXT FOR FINANCIAL SERVICES? GREAT CX.

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Jeff Woodland, Director of Verticals at Five9

 

The financial services industry has always been behind other sectors when it comes to digital transformation. Ingrained cultures alongside ancient financial institutes with legacy systems are tough cookies to crack. However, due to impacts from changing customer expectations, banks and other financial services companies have had to do a complete 180 on what they once thought was good customer service. Other sectors have revolutionised the customer experience model, and the financial services sector is now following suit.

Many people find it difficult to cope with the stress that comes with managing money. Our world revolves around our finances and is essential to everyday living. Being able to speak with a human when they need to discuss concerns and ask questions can put customers mind at ease. Similarly, receiving easily accessible services on whatever channel a customer chooses can improve the customer journey and build brand loyalty.

Those unable to connect customers through the right channel at the right time may begin to see huge impact on customer service that can be detrimental to reputations.

 

Scale and innovation is essential

Challenger banks like Monzo and Revolut also set a new standard for legacy banks. They offered a different way of banking with digital at the heart and this challenged traditional customer service. As such, traditional banks had to step up. Through scale and innovation, big banks have made significant gains in customer satisfaction over the last decade.

Digital transformation is paying off for financial services but it has also left siloed data and applications which are in need of integration in order to offer the most seamless experience for customers. Technology is essential when it comes to customer experience, but unless it is integrated and connects to all core systems, it cannot deliver its intended value – and may be more costly than it’s worth.

 

AI for everyday

That consumers have grown increasingly comfortable with interacting with smart devices in a natural and conversational manner shows how this tech can be done right. As a result, there has been a huge shift in consumer behaviour over the last few years, with research suggesting that shopping through voice-activated devices in the UK will be worth £3.2bn by 2022. Customers have come to expect virtual assistants at all points of the purchase journey – and financial services is no exception.

Whether it is through an AI chatbot, over SMS or via email, being able to get a quick, 24/7 response to questions helps to put a customer’s mind at ease. IVAs (Intelligent Virtual Assistants), are a prime example. They are automated, self-service applications that offer capabilities similar to human service and support agents but unlike their human counterparts, they just never rest or take holidays. This means that the customer can be looked after and responded to 24/7, offering that seamless experience that they don’t just want but have come to expect. 

The benefit isn’t just for the customer, however. As adoption of IVAs increases in the financial sector, the benefits are ten-fold for businesses too. Being able to offer automated responses to questions, and only having to use agents for bigger queries and complex issues, frees up valuable time for human agents to focus on more urgent customer enquiries. It also reduces average wait times for customers during busy hours, all whilst delivering a more human experience – one that isn’t actually human.

 

Creating seamless experiences

While consumers value speed, convenience and the ability to get quick turnaround answers digitally, for financial services, the telephone is still the most used channel when it comes to dealing with personal matters. This is where human contact and technology have to marry up perfectly in order to give the customer a seamless experience.

Customers want to be able to use digital services for tasks such as getting approval for credit cards or making an online transfer, but when it comes to getting a mortgage or making investments, being able to speak to someone is essential. It sounds simple, but too often businesses get it wrong.

For example, customers can get so far online and then have to speak to an advisor. If they get through to the advisor, they then have to repeat what they have already done online. This can be a huge pain point for customers.

This is where technology should be used to enhance human contact. Being able to provide an advisor with an overview of what the customer has already filled out and completed will help create that all important seamless experience. To get it right, financial services have to focus on the contact centre.

 

Give customers what they want

Now is the time for the contact centre to act as a central “hub” for customers to receive the best experience possible across all channels. Cloud contact centres are more advanced than ever, implementing AI is seeing the industry transform and provide more than the traditional customer services experience.

Those that want to deliver exceptional customer experiences must provide customers with what they want, when they want it. The cloud contact centre is here to deliver integrated services that provide quick, easy, convenient experiences for customers.

 

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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