The partnership will allow Rising Edge to accelerate their business and drive efficiencies with streamlined submissions processing, removing the need for underwriters and underwriting assistants to manually input submissions.

“We are impressed by the ability of the mea platform to accelerate our submissions processing through their ready to use solution” said Philippe Gouraud, Rising Edge’s CEO. “This is enhancing our proprietary digital operating platform. By further digitalising the underwriting information chain, we are able to further accelerate delivering our quotes to our brokers, in a very agile and efficient way.  This is a significant step in our tech & data strategy.”

Martin Henley, CEO at mea, commented, “Our technology has been designed specifically for the Insurance industry, helping solve the underlying industry problem with productivity. In a high-profile industry such as D&O insurance, ensuring that submissions are accurately and quickly processed is crucial. Currently, manual processes are holding the industry back. By utilising our platform and technological expertise, Rising Edge will free up valuable time and be able to focus on building their business.”

“We liked that there was little project effort to start using the mea solution, as it worked instantly. The team have worked together brilliantly to implement this solution which is helping our business to go from strength to strength” said Miles Murphy, Rising Edge COO and Finance Director.

The post Rising Edge collabora con mea per potenziare la sua piattaforma di sottoscrizione digitale appeared first on mea platform.

If you look at how much insurers have spent on technology over the last decade, return on investment is negligible. In fact, it’s becoming clearer that insurtech is caught in a paradox of intentions whereby each organisation, convinced of its ability to add value to the insurer, only adds to a growing web of complexity – a spaghetti mess of systems.

The reality is that the sector has a growing, as opposed to shrinking, technology problem. Almost all insurers have too many systems, doing too many different things, which eats into internal resources instead of saving time and making life easier.

Clever, but counterproductive

It starts with a misalignment of needs. Insurers are first and foremost experts in understanding and writing risks. They’re not experts in understanding and implementing technology, so they know they need to look externally for technology. Insurtechs on the other hand are usually built by younger, tech professionals, and backed by Venture Capital firms, neither of which have the sector experience to truly understand the needs of insurers.

And all of this takes time. Behind every new system lies a lengthy decision process, likely with several rounds of pitches and meetings involving senior stakeholders. Then, once decided, the technology is seldom ready to go and the insurer has to work closely with the tech firm to implement their product and get it working. It is a flawed process that can be a colossal distraction and stands in the way of actual progress.

This unfortunate dynamic also means insurtechs fail to see how they are contributing to complexity. Each company usually solves one specific problem – one piece of a very large jigsaw. As a result, the market is saturated with a range of different solutions that cannot yet interact and complement one-another.

Part of the problem here is that both parties use very different terminology and vocabulary. For example, common tech buzz-phrases like API integration, full stack, no-code, and cloud-based, are terms that are little-understood outside of the tech teams in insurance firms.

The misalignment is, therefore, largely a matter of communication. The fact is insurtechs are only really guessing what insurers need because of a structural lack of dialogue and collaboration when developing solutions.

As a consequence, insurtechs tend to be technology-led businesses. They start with clever technology and then try to apply it to insurance, which often cannot be scaled to meet demand. It should be the other way around: find out what the insurer needs, then develop the technology.

Stuck on outdated architecture

That being said, the internal structure of the insurance business also stands in the way of progress. The problem is – if you will – far more systemic.

All insurance companies have a central record policy administration system, most of which were built thirty or even forty years ago and are therefore very outdated. Not only does their limited functionality mean other technology is often needed, but insurers will typically build new systems – whether developed in-house or sourced externally – on top of this outdated architecture instead of evolving and upgrading it.

Over year and years, teams within the insurance business have pushed for new technologies such as CRM tools, claims platforms, or sophisticated analytics, as they have come to market. Each system then needs people to set it up and run it, further taking up internal resources.

While this might provide the relevant team with the tech they needed in the short term, it is detrimental for the long term. The insurer – especially those operating across multiple regions and product lines – ends up with an ever-expanding myriad of individual systems plugged into a clunky central architecture. As a benchmark, the last business where I was Chief Information Officer had 2,500 systems across the business. But most have a similar problem.

Only now is the scale of the problem emerging. To move off old central systems would mean re-building all systems onto a new central infrastructure, essentially starting from scratch, which it turns out would cost many insurers more than the value of their entire company. As such, many companies have dug themselves into a hole, paralysed of real growth

Something has to change. But how can insurers move off central systems when the cost is so high?

Putting an end to the spaghetti

The task is far from simple and there is no magic solution. But what is clear is that insurtech must fundamentally reconsider its objective and focus on the needs of the insurer.

Boil it down and the primary goal of insurtech is to improve processes across the insurance value chain. So, as a starting point, insurtechs must think more holistically about the processes across the risk cycle and make each step smoother.

Conceptually, we insurers should be able to simply tap into a service that drastically cuts down processes across the business. If they could streamline processes and scale more easily, it could take points off the insurers combined ratio and fuel growth.

The post Spaghetti di sistema: Il paradosso dell’insurtech appeared first on mea platform.

Lohit joins mea after more than a year of advising and supporting mea in its development. He has more than 20 years of IT experience across multiple domains, and currently holds 35 patents and 16 publications for inventions across multiple domains.

He joins mea from IBM, where he has spent the last 17 years, and was most recently Principal Architect and CTO for Kyndryl Private Cloud offering, leading the formation of a team, providing deep technical leadership, defining best practices, designing and implementing complex solutions across multiple cloud offerings and Hybrid Multi-Cloud engagements.

As CTO, he will oversee the growth of mea’s existing 4 modules and, working closely with experienced insurance experts within mea, will steer the development of new modules that will drive efficiency within insurance businesses.

Commenting on his new role with mea, Lohit said:

“I’m delighted to join the mea full time, having spent the last year supporting the company and seeing the innovative solutions they are creating. I can really see how we can use AI to make a big difference to the insurance industry and eliminate the cumbersome processes used by most participants. My experience and skills fall perfectly in line with mea’s ambitions to allow insurers the flexibility to scale capacity as needed, safely, securely and efficiently.”

Martin Henley, CEO of mea platform, commented:

“We are thrilled to welcome Lohit onto the mea team. Insurance is an industry locked in its own past, and Lohit’s technical expertise will help us on our mission to help insurance teleport into a lucrative future by automating the manual processes holding insurance business underwriters back. His passion for innovation will continue to drive efficiency and growth among our clients.”

The post mea assume un nuovo CTO per promuovere l’efficienza nel settore assicurativo appeared first on mea platform.

Un modo in cui l’IA può essere utilizzata nelle assicurazioni commerciali è attraverso modelli preaddestrati. I modelli pre-addestrati sono insiemi di dati che sono stati inseriti in un algoritmo e ai quali è stato insegnato come raggiungere le conclusioni sulla base di tali input. Si chiamano preaddestrati perché, una volta impostati, è sufficiente accenderli e lasciarli agire: non è necessario alcun addestramento!

I modelli pre-addestrati sono particolarmente utili quando si lavora con prodotti assicurativi commerciali speciali, come la responsabilità civile informatica o la responsabilità civile degli amministratori e dei dirigenti (D&O). Rischi non standard come questi richiedono spesso competenze aggiuntive da parte di sottoscrittori e altri esperti che non li conoscono. Utilizzando modelli pre-addestrati invece di assumere qualcuno appositamente per questo lavoro, potete risparmiare tempo e denaro, ottenendo comunque i migliori risultati possibili dalle vostre attività di policyholder experience (CPE).

L’IA pre-addestrata è un tipo di apprendimento automatico che utilizza un set di dati per addestrare un algoritmo e può essere utilizzata nelle compagnie assicurative per prevedere la probabilità che si verifichino determinati eventi, come il rinnovo della polizza da parte di un cliente.

I dati utilizzati per addestrare l’algoritmo provengono da esperienze passate e possono includere dati demografici e comportamenti precedenti. Queste informazioni vengono poi utilizzate per creare un modello in grado di prevedere le azioni future sulla base di quelle passate.

L’intelligenza artificiale è una tecnologia che può essere utilizzata per automatizzare attività come l’elaborazione delle richieste di risarcimento assicurativo o la gestione dei sinistri e utilizza l’apprendimento automatico per imparare da grandi insiemi di dati ed eseguire attività basate sulla comprensione dei dati. È stato utilizzato nelle compagnie di assicurazione fin dagli anni ’90, ma solo di recente è stato adottato in modo più diffuso dal settore assicurativo.

Ecco alcuni degli usi più interessanti dell’IA pre-addestrata:

-Può essere utilizzato per ridurre le frodi, facendo risparmiare alle aziende milioni di dollari di perdite ogni anno.

-È stato dimostrato che aumenta le vendite fino al 20%. Ciò significa che le aziende che utilizzano l’IA pre-addestrata vedranno probabilmente un aumento dei ricavi e una riduzione dei costi associati alle frodi.

L’intelligenza artificiale pre-addestrata è un tipo di intelligenza artificiale che è stata addestrata su un ampio set di dati. Questi sistemi sono pre-programmati per eseguire compiti specifici che possono essere utilizzati per fornire soluzioni alle aziende. L’IA è un campo in rapida evoluzione e le aziende devono stare al passo con questi cambiamenti per rimanere competitive, poiché l’IA può imparare dai propri errori e migliorare nel tempo.

Elaborazione del linguaggio naturale (NLP): Questo tipo di IA pre-addestrata utilizza algoritmi di elaborazione del linguaggio naturale che sono stati addestrati su grandi quantità di dati testuali, come articoli di notizie o post di blog con parole chiave specifiche. Questi algoritmi consentono ai computer di comprendere il contesto delle parole, in modo da poterle identificare meglio all’interno di un determinato contenuto. La PNL può essere utilizzata dalle compagnie assicurative quando vogliono ottenere le richieste online attraverso un portale online o un’applicazione mobile, invece di far chiamare direttamente i rappresentanti del servizio clienti ogni volta che succede qualcosa.

Le compagnie assicurative hanno sempre avuto a che fare con l’incertezza, ma l’IA le sta aiutando a gestirla in modo nuovo. Invece di affidarsi al giudizio umano, le compagnie assicurative utilizzano l’IA per fare previsioni su eventi futuri utilizzando dati storici e modelli statistici. Questa tecnologia è chiamata “pre-addestrata” perché viene fornita con tutte le informazioni che possono essere utilizzate per fare previsioni.

L’intelligenza artificiale viene utilizzata anche per aiutare gli assicuratori a comprendere il rischio identificando modelli nei dati dei sinistri, il che li aiuta a prendere decisioni migliori su quanto far pagare la copertura.

In conclusione, abbiamo visto che l’IA ha la capacità di rivoluzionare le assicurazioni commerciali. I modelli pre-addestrati possono aiutarci a comprendere meglio i nostri dati e a fornire approfondimenti a cui altrimenti non avremmo avuto accesso. Siamo entusiasti dei cambiamenti che questo porterà e non vediamo l’ora di continuare a esplorare il modo in cui l’IA può essere utilizzata nel nostro settore.

The post IA pre-addestrata: spiegazioni appeared first on mea platform.