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Sweet talking: how chatbots help banks become smarter

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Cathal McGloin, CEO of ServisBOT

 

Traditional banks are under pressure from increasing regulation and challenger banks. This has led to a sharper focus on unit economics and the increasing application of artificial intelligence and automation technologies that lower service costs while still meeting customers’ needs.

Chatbots: The new voice of banking has arrived

 

To maintain customer service levels while reducing service costs, an army of chatbots are offering game-changing opportunities for financial institutions to transform how they engage with customers across all touch points in their financial interactions. Whether it’s a request to provide a customer with the balance on their account or activate a newly-issued credit card; acquiring new customers by bringing them through a smooth application and approval process, or promoting new banking products and loyalty programs, chatbots can handle a multitude of customer interactions.

New channels of communication, enabled by social messaging platforms, voice-activated assistants, and mobile devices, have created new and exciting possibilities that are increasingly centered on conversations.  Powered by Natural Language Processing (NLP) and artificial intelligence (AI) technology, automated customer interactions now enable human-like chat and fluid conversations.

For the consumer, smart chatbots bring greater convenience, lower friction, and increased accessibility for their banking needs. For banks and other financial service providers, it brings a new wave of innovation centered around customer conversations.  Now, financial institutions can literally have a voice and deliver their services to customers in more convenient and automated ways, across multiple touchpoints and communication channels.

Chatbots: evolving toward conversational AI

Intelligent banking chatbots can interpret customer intent, understand exactly what they want, elicit any additional information needed, and execute the necessary tasks, all in a single seamless chat. Rather than limiting customers to pre-defined banking processes, departmental siloes, structured forms and menus, cluttered websites, and contact centre availability, a single automated conversation can trigger the tasks that are needed to fulfill the customer’s need. This is what is commonly termed as Conversational AI.

Banking bots help streamline and automate customer-facing processes, lowering the cost of service delivery. Take, for example the simple task of authorising and activating a customer’s newly-issued credit card. A chatbot could be used to replace the call to the contact centre, getting the card activated quickly and easily and without the need for human agent intervention.

In addition to using bots simply to further automate specific tasks and make them available 24/7, there are far-reaching opportunities to use conversational AI to transform customer engagement across the banking organisation. By allowing the automated conversation flow to call on different business processes, the customer can move across various service channels to fulfill their financial service needs.

For example, a customer may request their bank balance by voice over Siri, or Amazon Alexa. The account bot can move the conversation to the customer’s preferred messaging channel, sending them their statement balance. This chat could then call on other bots to send a reminder on the upcoming credit card payment, promote a new product or offer, or to ask the customer if they have any other banking need.

Since conversations are more fluid and less structured they lend themselves to more versatile engagement. They also remove a lot of the friction that frustrates customers when they are trying to complete multiple tasks by enabling this in a single conversational session.

A new way of working

Speaking in a recent television interview, Monzo CEO, Tom Blomfield, reported that, while traditional banks are investing £150 per customer per year to maintain each customer account, his challenger bank can service an account for a tenth of that.

Using a range of customer interaction channels has been key to this cost reduction. Monzo uses a combination of in-app chat, social media, browser-based FAQs and traditional call centre agents, to help customers to find answers in the most customer-friendly and cost-efficient way.

Writing in Monzo’s annual report, Blomfield wrote, “We’ve also been able to make savings by helping our customer support team become more efficient. Together, this has helped us lower the cost per account to around £15.

About £10 of this cost goes towards providing fast, friendly support: the team who speak to our customers and solve their problems every day, in-app, over the phone and on social media. We see that £10 as an investment that lets us provide an effective, delightful service that’s reflected in a Net Promoter Score of almost +80.”

With banks under increasing pressure from new and agile competitors and a more demanding and tech-savvy generation of banking customers, creating superior experiences is a must. Chat, combined with the power of AI, is the key.

Chatbots: go beyond the contact centre

There are multiple ways in which chatbots can transform customer-facing interactions in the banking industry. They can be used to automate tasks to assist with most customers’ financial service interactions. However, chatbots can be extended beyond contact centres to encompass any other operational area involving customer interaction.

Within financial operations, any process that is customer-facing, whether approving a customer for a credit card, fraud prevention, or credit management, are all potential use cases for bots. JPMorgan Chase uses chatbots to streamline back-office operations. Its contract intelligence software, COIN, scans commercial agreements and has saved more than 360,000 hours of employee time. The chatbot also assists employees with regular IT requests such as resetting passwords.

For credit card providers, collections management is an important operational issue to reduce the number of delinquent accounts and bad debts that impact cash flow. However, connecting with customers often proves difficult when arranging collections. This situation is exacerbated by reduced engagement levels via phone.

A collections bot can proactively contact late payers at convenient times via messaging, which has measurably higher engagement rates. The bot can make it easy for customers to make a payment within a single chat session and can guide the customer to enrol in payment programmes, such as autopay or prepaid cards. A collections bot can also take the necessary actions to reduce a credit limit or suspend the card and can send multiple reminders to ensure that payment is made, without increasing service costs to the credit card provider.

Banking chatbot use cases, both inbound and outbound, can be tailored to customer segments, by geography, and by operational department. Conversational AI can transform how banks engage with customers, not just in single siloed transactions but in more fluid and flexible ways that can make banks more competitive and build their brand identity.

Chatbots: a banking ambassador for customer loyalty

From the early days of print and telephone to the emergence of the internet and online banking, financial institutions have engaged with their customers using a range of communication channels. Conversational AI represents one of the biggest shifts in user interfaces since the introduction of the ATM forty years ago.

To meet the needs of millennial customers, banks need to up the ante on convenience, trust, and personalization, applying the latest technology to attract and retain this growing customer segment. Wells Fargo uses a chatbot to allow customers to check their latest transactions over Facebook Messenger. Using technology to pre-empt questions, Monzo reduced the number of customers that needed to get in touch with its contact centre by 33 per centin three months.

Voice-activated interfaces including Siri, Echo, Cortana and Google Home and message-based interfaces such as SMS, Facebook Messenger and WhatsApp, are increasingly shaping how banks engage with these customers and create brand loyalty through smart conversations. By keeping the conversation flowing, AI can help banks maintain the vital balance of keeping customers happy while managing costs.”

 

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Finance

HOW FINANCIAL ORGANIZATIONS CAN PROTECT THEIR DATA

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Yuval Wollman, President, CyberProof and Chief Cyber Officer, UST

 

Top executives from Wall Street’s largest banks pinpointed cybersecurity as the greatest threat to America’s financial system, at a Congressional hearing that took place in May.

The concern of financial industry leaders with cyber-attacks is neither surprising, nor new. The attraction of cybercriminals to banks and other financial institutions makes sense, given the fact that the financial sector functions as gatekeepers – not just of financial assets, but also of valuable Personally identifiable information (PII).

Threat actors are attracted to attack financial institutions to earn a profit through increasingly sophisticated attacks that range from ransomware attacks to identity theft. But while the threat continues to grow, there is much that can be done to mitigate the risks.

 

The Downsides of Digital Banking

The number of attacks on financial institutions increased sharply in the last two years due to the upheavals wrought by COVID-19, which prompted a dramatic rise in the number of online transactions.

With so much of today’s financial transactions done on both web and mobile devices, threat actors have more opportunities than ever before. Take, for example, the growing importance of Man in the Middle (MITM) Attacks, which impersonate another party online and give criminals access to personal data, passwords, and banking details.

With the widespread adoption of digital banking, consumers have become increasingly worried about cyber-attack. As a result, there’s growing demand to create better consumer protection laws that respond to the rapidly evolving technology. The U.S. Federal Trade Commission (FTC), for example, recently strengthened security safeguards for consumer financial information.

 

It’s Not “Just” About the Money

Financial organizations are at risk not just from threat actors looking for profit, but also from nation-states and hacktivists acting out of idealistic motives or as a means of achieving specific political ends.

The most famous examples of this type of attack include Russia’s 2016 attack on Ukraine’s electric grid and North Korea’s 2017 attack on Britain’s National Health Service.

Because of the extent of the damage that this type of attack could cause, NATO established cyberspace as the “fifth domain of warfare” in 2016. It developed a definition of when foreign factions are banned from attacking financial institutions, due to the fear that this type of attack could directly lead to a country’s destabilization.

 

Recognizing Risk Factors

The digital transformation of financial services helps banks and other financial institutions provide more a more convenient customer experience.

And while significant customer demand has led many banks to implement changes such as the transition from legacy to cloud-based solutions, these shifts also have the potential to create additional security risks.

For example, if we’re talking specifically about cloud migration, there’s need for additional security layers to protect organizations working with public cloud providers from the range of attacks targeting the financial sector: ransomware, account takeover, data theft and manipulation, phishing attacks, identity theft, and more.

Another example is the extensive use of third-party vendors, which has increased the risk of attack for organizations in the financial sector. Because third-party vendors enlarge the attack surface, they create more entry points to the system and make it harder to protect customer data.

 

Accelerating Detection & Response

By adopting an agile approach that supports continuous improvement, financial organizations can facilitate proactive identification of evolving threats and vulnerabilities in the wild. More specifically, by placing an emphasis on use case optimization – which starts by mapping out an organization’s threat detection gaps to a framework such as MITRE ATT&CK – enterprises can prioritize threats and invest their time and resources in mitigating risk more effectively.

For organizations transitioning to the cloud, what’s key is managing the migration process in a way that provides optimal visibility in the cloud and supports ongoing optimization at the enterprise level. Digital playbooks are a crucial tool in providing improved detection and response, creating automated or guided responses that allow faster, more effective, collaborative action.

The development and regular review of incident response plans similarly allows for efficient response in emergency situations and helps reduce the business impact of cyber-attacks.

 

Targeted Threat Intelligence

Threat intelligence that’s tailored to the financial services sector is another key component of timely detection and response. By working with expert Cyber Threat Intelligence (CTI) services, organizations can obtain up-to-date information about industry-specific threats in real time – information that is a highly valuable tool in strengthening the defense of an enterprise.

 

Cyber Hygiene

Employees make mistakes; after all, it’s only human. But these errors can lead to massive data breaches. For example, when someone clicks on a phishing email or leaves passwords for a company computer on a slip of paper that’s easily seen by the wrong person, the damage can be astronomical.

Providing regular cybersecurity training programs for employees can help minimize the risk of an accidental or careless action leading to cyber-attack. To be effective, training programs should not only explain how to spot cybersecurity risks like phishing emails but should also discuss how and where it’s safe to access company information.

Aside from employee training, there are fundamental cybersecurity-related decisions that should be implemented at the enterprise level such as Zero Trust, DevSecOps, and multi-factor authentication (MFA). From a policy perspective, for example, it’s crucial to enforce MFA for all applications. Moreover, technology-related vulnerabilities can be minimized through frequent patching and updates for systems. Audits, as well as vulnerability and penetration tests, must be conducted regularly.

 

For the Financial Sector, “Best Practices” are Key

With the growth in number and complexity of cybersecurity attacks on financial organizations and the increased risk of nation-state attacks, proactively approaching the question of cybersecurity and implementing “best practices” makes the difference in reducing the degree of risk to an enterprise.

By modernizing the SOC with a carefully navigated migration to the cloud, adopting continuous improvement of use cases and the development of digital playbooks that improve detection and response – as well as by leveraging targeted threat intelligence and maintaining strong cyber hygiene – enterprises can put themselves in a stronger position to minimize the potential business impact of a cyber-attack on their organizations.

 

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IF IT’S A LOSS, YOU’RE TOO LATE – WHY THE INSURANCE INDUSTRY NEEDS TO FOCUS ON FIRST NOTIFICATION OF RISK

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Simon Dicks, Insurance Channel Manager EMEA, Lytx

 

Insuring commercial fleets can be an expensive business. Average repair costs have increased by up to 40% in the past 8 years and disputes about who was responsible can drive up expenditure for both fleets and insurers.

Part of the problem is that the insurance industry hasn’t had the tools to forecast costs and premiums accurately enough in this sector. Underwriting decisions are still made in the same way they always have been, by looking back at historical data from previous years. This approach simply isn’t giving insurance companies an accurate indication of potential risk – or a proper indication of the impact of driver behaviour.

Technology is helping insurers to an extent by providing information about First Notification of Loss (FNOL) – automatically sending notifications when unusual G-force readings are captured within a black box tracking device as a result of sudden braking or impact. This is good, but far better is the ability to use proactive technology to detect when an incident is at risk of occurring and when a driver is distracted.

The only way to address this is to put a highly accurate level of camera technology both inside and outside cabs, supported by sophisticated technologies such as Machine Vision (ML) and Artificial Intelligence (AI). This way, we can see not just that an incident has happened, but why it happened. What’s more, we can assess risk before an accident happens at all and prevent it happening in the first place. We call this First Notification of Risk (FNOR) – and it’s a whole step up from FNOL.

Machine Vision scans the internal and external environment of the vehicle to identify distracted driving behaviours such as mobile phone use, eating, drinking, smoking, inattentive behaviour or failure to wear a seatbelt. AI, comparing the behaviour against a vast bank of accumulated data, is then able to determine the riskiness of that situation and whether it needs to be flagged to the fleet manager, driver, or insurer via a short video clip. The big difference in this approach is that it’s proactive, not reactive. For the first time, fleets and insurers can identify adverse driving and distracted driving in real-time for the first time.

This includes the ability to alert drivers of any momentary slip-ups or distracted behaviours. Using the same technology, drivers will receive an audio or visual alert to help keep them on track and to lessen the likelihood of a moment’s distraction becoming anything more.

When insurers have access to these insights, they can also start to see patterns from the data over time. For example, a fleet manager might start to see that there’s a peak in risky driving behaviours on a Friday afternoon when lots of drivers are rushing to finish for the weekend. As a result, they may decide to spread the shifts differently so as to avoid that pattern of behaviour.

When insurers are only looking at FNOL, it’s already too late. A driver could be unthinkingly driving whilst smoking, on their phone, and nobody would never know. Whereas with FNOR, both managers and insurers are provided with insights that remove the guesswork, and underwriters have the information they need to assess risk with far greater precision.

There’s still a long way to go in making the move towards FNOR. With so many different companies selling cameras and telematics systems and producing information in hundreds of different formats, claims data will have to be standardised before the sector can really transform. However, by starting to embrace ideas like FNOR, the industry can move towards a solution that saves them time, money and lives.

To find out more, visit  www.lytx.com/FNOR

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