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If your company handles insurance-related customer interactions, it’s time to consider Artificial Intelligence (AI). As industries go, the insurance sector is actually very advanced when it comes to AI adoption; in fact, a recent Microsoft report showed that nearly three-quarters (72%) of banks, insurance firms and other financial institutions are using such technologies.

While awareness and pilot implementations within the industry are definitely on the rise, there are still many executives that are still struggling to identify the best applications for their business. Indeed, almost 40% of all practitioners who have not yet invested in AI don’t know where AI can be used in their business, according to Deloitte.

To explore how AI can enable better customer interactions for insurers, we explore three key considerations for insurers that are thinking about implementing AI for customer services.


What service do I want to provide?

The first hurdle for any business when determining whether AI can improve customer interactions is deciding what level of service they want to provide. There are a number of different types of digital assistants on the market – and not all bots are made equal.

Insurers can easily find themselves comparing apples and pears when looking at AI solutions. So, it’s critical to start by looking at the different available solutions – from static chatbots, to cognitive AI agents – to consider the benefits of each solution and determine which can deliver the best experience to their customers.

Martin Linstrom

Cognitive agents use advanced Natural Language Understanding (NLU) to navigate complex and foreign words and phrases to converse with customers. Unlike static chatbots, cognitive agents go beyond words to determine user intent. Once they determine a customer’s intent or intents, cognitive agents can handle multiple contexts within one conversation. This delivers a more complex, prolonged, and multi-stage conversations.

Cognitive agents can also be trained to gain a specialised understanding of a certain marketplace, and adapt to the role, so they can respond to customer questions across a wide variety of subject areas, including renters, property, marine, auto, and life insurance.

Chatbots don’t offer this level of sophistication. They operate from programmed scripts that allow them to complete basic tasks. If customers veer from this script or use language that is foreign to the chatbots, it will immediately escalate the transaction to a human colleague.  This extends the interaction unnecessarily and creates a frustrating customer experience.

Finally, many customer engagements for insurers come at difficult moments in their lives. They may have just been burgled, a relative may have died, or they could have just had a car accident. It’s therefore important for any automated solution to recognise and react to the emotional state of the customer. Some cognitive AI agents also have emotional intelligence built in, allowing them to understand the customers’ tone and mood throughout the interaction. This enables it to display empathy and detects sentiment in order to reassure customers with appropriate phrases and comments when needed.


How can a cognitive agent help?

Every deployment of a cognitive agent is unique, so insurers need to think about the best way that a cognitive agent can help them and their employees. Here are just a few ways that cognitive agents can be deployed in an organisation:

Always-on customer care

Cognitive agents’ services can be delivered across multiple channels (web, phone, text, chat, etc.) 24/7/365, providing limitless support capacity. This ensures a customer never waits long for quality service even during spikes in customer call volume.

In fact, IPsoft’s Amelia’s transactional processing and scaling ability has enabled insurers to reduce their support costs by up to one-third and reinvest those savings back into the business. Because of her scalability, transaction and support resolution, times are drastically reduced. She can also reach accuracy levels in excess of 95% in managing certain conversations and policy transactions.

Exemplary executor

Cognitive agents are expert at gathering, verifying, and processing customer information. When customer facing, this enables them to have natural conversations with customers about insurance needs and personal habits in order to offer policy information and quotes.

With their deeper industry understanding and ability to engage in a real dialogue, Cognitive AI agents can make recommendations and execute transactions faster than humans with a personalised touch. This typically eliminates the need for a customer to visit an agent’s office or make a support call, as the cognitive agent can handle most frequent customer queries and transactions, such as incident registrations, policy recommendations, deductible and payment inquiries, rider recommendations, and a whole lot more.

The whisper agent

Cognitive agents’ ability to learn and improve over time helps them collaborate with human customer service colleagues, unlike static, low-level chatbots.

Non-licensed agents are not permitted to advise on or sell certain products and services. When those agents receive a call and need to service a customer, they can quickly interact with the cognitive agent behind the scenes and determine whether they can assist that customer. If they can, the cognitive agent uses its knowledge to coach the agent through the customer engagement; if not, the cognitive agent helps the human agent refer the customer to a colleague who can.

In the end, the customer receives correct information and an efficient service experience, the agent is confident that they’ve done the right thing, and the company knows their employees are complying with all proper and legal procedures.

Fraud detection

With their advanced analytics, cognitive agents can also analyse a customer’s submitted information and help human agents determine whether the data is up-to-date and accurate. This then allows them to make recommendations on whether a policy should be issued, for what amount and at what premium level. As knowledge is accumulated over time, cognitive agents are well placed to recognise anomalies that may indicate fraud and report them to their human colleagues.


Does it really work?

There’s no question that cognitive agents hold all the promise for better customer interactions in insurance. But the million-dollar question is “does it really work?”.

One large insurer seeing the benefits of using cognitive agents to support their customer interactions is Allstate. The largest publicly held personal lines insurer in the US, Allstate first deployed cognitive AI agent, Amelia in September 2017. She has collaborated with Allstate live agents on more than three million calls. She leads agents through step-by-step procedures on a variety of support issues, including policy details and policyholder information.

Trained on almost 50 different insurance topics for Allstate, Amelia has lowered call duration from 4.6 to 4.2 minutes and 75% of customer inquiries have been solved during the first call, compared with 67% prior to Amelia’s hiring. In one month alone, Amelia assisted on almost 250,000 calls. Also, 99% of Allstate agents who worked with Amelia said they were completely satisfied with their interactions with her.


What are you waiting for?

Cognitive agents are a proven, enterprise-ready and scalable solution that are taking insurers’ customer interactions to the next level. Those insurers that have already invested are taking advantage of the competitive edge that it has given them against other companies that are hesitant about making investments in AI.

But given the current market trends and shifting consumer tastes, it won’t be long before customer expect and demand the always-on and superior service that cognitive agents provide both directly and behind the scenes. The hybrid workforce of human and digital agents is the future of customer engagements, so now is the time for insurers to investigate and invest in AI is now.


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Emerging technology will power long-term sustainability within the UK banking industry 




By Peter-Jan Van De Venn, VP Global Digital Banking at Hexaware Mobiquity.


Sustainability has been a big focus for the banking industry in recent years, with the issue becoming increasingly important for consumers. It’s no wonder that sustainability has become baked into the purposes of almost every bank, from Natwest to HSBC.

However, the economic uncertainty of the last year has led to many banks putting it on the back burner. Challenging market conditions have forced financial institutions to change their priorities to concentrate on protecting the bottom line. Our research found there’s been a significant drop in the number of UK banks saying that sustainability remains a key business strategy. 12 months ago it was a major priority for 100 per cent of banks, but now that number has shrunk to 60 percent.

Whilst it’s understandable that banks are feeling the pressure at the moment, there’s a risk that they will miss out if they hit the pause button. From cost savings brought by innovative digital products and services, to improved brand reputation and increased profitability, there are a lot of longer-term benefits they could be failing to unlock. So how can they keep moving forward?

Losing momentum

Emerging technology holds the key to their success, with the power to disrupt current behaviours and promote a more sustainable culture. Banks are already aware of this, with 76 percent using digital transformation to drive sustainability, but a lack of leadership has made it difficult to build momentum in the last 12 months. Currently just over half (54 percent) of banks have tasked an executive at board level with overseeing sustainability – way down from 83% just 12 months ago.

This lack of board authority means banks are struggling to engage the entire organisation to move ahead with sustainable initiatives. As a result, almost two-thirds of banks are seeing progress slow, admitting they are not actively taking steps to foster more sustainable behaviours throughout the organisation. Those that have taken their foot off the gas need to find a way to move forward again.

No time for standing still

Banks know that technology can drive sustainable behaviour. For instance, many of them are already encouraging their workforce to work remotely, as a way of reducing travel. This has two benefits – not only does it cut the costs of running physical offices at full capacity, but also reduces the bank’s carbon footprint. There has never been a better time to invest in technology to drive more sustainable behaviours.

New digital products and services can also extend the benefits beyond employees to encompass the wider customer base. A fair number of banks are already investing to make this happen. More than a third (35 percent) of banking organisations are using Machine Learning (ML), Artificial Intelligence (AI), cloud and analytics to make digital services more easily accessible. Investment in these technologies will be critical as the number of physical bank branches continues to decrease, with figures from Which? showing this is taking place at a rate of 54 branch closures each month.

Hitting environmental and social responsibility goals

Emerging technologies can also help banks keep pace with tightening ESG rules and regulations. Banks are faced with demands for increasingly granular reporting and transparency on ESG – demanding a new approach. In line, 41% of them are developing data visualisation tools to improve stakeholder engagement and understanding of ESG risks and opportunities, while 37% are using machine learning and artificial intelligence to identify and track ESG risks and opportunities across a wide range of data sources.

More than one in three are also using the blockchain to improve transparency and traceability in supply chains, and implementing digital tools and platforms to collect, analyse, and report ESG data and metrics in a standardised and consistent manner. All these applications of emerging technology will put banks on track to address global environmental challenges and unlock a greener future.

Long-term sustainability

As the economic pressures hopefully start to subside, increasing numbers of banks will start investigating how they can use emerging technologies to provide engaging experiences and value-added services for customers, to drive greater revenue and efficiencies.

Whilst banks are right to focus on their revenue under difficult trading conditions, it’s important they don’t miss out on the long-term benefits that sustainability can bring. To capitalise on this, banks must keep pushing the boundaries and invest in emerging innovations to drive more sustainable banking behaviours, benefiting the planet and driving great digital experiences for customers.

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The Future of Banking: Streamlined Cash Management for ATMs




Gaetano Ziri, Innovation Manager, Auriga


“Maintaining free access to cash for the community demands robust strategies to mitigate the escalating costs incurred by banks and ATM operators in handling cash. A pivotal step in this direction is modernising cash management systems to foster efficiency and reduce operational costs.

Back in 2018, a report by McKinsey underscored the urgent need to overhaul the largely manual and disjointed systems relied upon by nearly half the banks worldwide for forecasting cash requirements at branches and ATMs. Despite the decrease in cash usage noted by the European Central Bank, the cost of managing cash has not abated, primarily due to surging labour costs.

To reconcile the demand for free access to cash with the requisite cost reductions, banks are increasingly turning towards tech-driven solutions in cash management that elevate service levels while driving down expenses.

The Complex Landscape of ATM Network Management

Operating a vast ATM network can be a double-edged sword for banks, simultaneously offering customer convenience and engendering considerable challenges, including substantial cash handling, management, transit and security costs. Each ATM embodies a multifaceted operation involving numerous cash transfer operatives, necessitating a coordinated strategy to forestall costly inefficiencies.

The remedy is a holistic, data-centric approach to streamline the management of intricate ATM networks and counter the escalating costs associated with cash access. The merits of such an approach, grounded in continuous data collection and analysis across ATM networks, encompass:

  • Strategic Planning: Leveraging real-time data to craft bespoke strategies for individual branches or regions, assuring optimal cash flow management and averting superfluous cash loading orders.
  • Operational Transparency: Facilitating stakeholders with instantaneous access to accounting and operational data relating to cash supply chains, thereby enabling timely interventions and adaptations.
  • Enhanced Customer Experience: Minimising ATM downtimes to guarantee uninterrupted cash access to customers, enhancing their banking experience.

Innovations in Cash Management: A Closer Look

So, how does this revolutionary cash management technology function? The answer lies in a series of sophisticated features that employ cutting-edge predictive analytics, automation, and data-driven decision-making:

  • Predictive Analysis: Forward-thinking solutions predict cash necessities of distinct units, offering precise demand and cash flow projections by considering variables such as seasonal fluctuations, holidays, and daily usage trends.
  • Automation and Monitoring: Swapping manual processes or basic mathematical functions with modern software solutions for cash management ushers in real-time monitoring and efficient intervention planning, which can potentially diminish order management costs by a significant margin, whilst improving precision and operational fluidity.
  • Optimised Cash Transit Management: Utilising predictive analytics to strategically plan cash restocks, thereby reducing the likelihood of ATMs depleting their cash reserves and improving customer satisfaction.
  • Data-Driven Decision Making: Availing a comprehensive dashboard to generate timely reports and monitor critical metrics facilitates strategic decision-making grounded in accurate data, substantially reducing residual cash stock in ATMs.

As the financial landscape evolves, banks and financial institutions are impelled to adapt and innovate. Traditional cash management approaches are increasingly becoming outdated, paving the way for modern, data-driven solutions. These not only embody a commitment to technological advancement but also signify a strategic movement towards future readiness.

Embracing such technologies promises streamlined operations, substantial cost reductions, and a superior customer experience, setting a new standard in ATM network management.”

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