Mohamed Hanini: Artificial Intelligence Can Accelerate the Mergers and Acquisitions Process

This is a transcript of his speech:

There are many millionaires in the world who have undergone numerous M&A cases. The experiences they endure during these processes are often profoundly painful, leading many to abandon the entire 'process' altogether—particularly when it comes to insurance products. This is precisely our fundamental concept: accelerating the relevant work processes.

How might we better address this challenge? In discussions with numerous individuals regarding financial products and insurance offerings, we developed a solution that ultimately provides clients with guidance and support. This includes explaining insurance products, delivering insurance quotes, and detailing other insurance options. I believe the best way to demonstrate this is through a live presentation. For instance, this product, as shown in the diagram, is cloud-based. Before commencing, I'll click through a consent agreement declaration – which may be inaccessible in China – then select 'Confirm Terms' to proceed. You can then pose questions such as 'What constitutes life insurance?' or 'What does life insurance cover?' You'll see it encompasses scenarios like accidental death, life insurance will provide coverage and compensation. The next step displays 'What life insurance options are available?' – essentially two main types.

We can also ask other questions, such as 'What are the differences between these life insurance policies?' – highlighting the distinctions between the two types. Here, if the customer requires a quote, they can state their need by typing 'I require a life insurance quote.' This transitions the process from the consultation phase to the assessment phase. For instance: 'Are you familiar with insurance?' I reply: 'No, I'm not.' 'What is your current age?' I enter: '38 years old.' 'Have you smoked in recent years?' 'No.' 'Do you have any debt issues?' Additionally, the section on "challenges faced" is crucial, as we need to make this system more consumer-friendly. It must be trainable and capable of conversational interaction.

For instance, "currently 27 years old" – naturally, it requires training. I wish to demonstrate this process: within three years, we can digitise these procedures. Take insurance, for example – we can process the entire dataset for insurers, enabling quotations within minutes. Traditionally, these processes take 30 days and involve numerous personnel. Consider, for instance, $89,000 in liquid assets. This is where natural language processing (NLP) comes into play – categorising consumer queries based on context. Thus, saying 'Hello' at the start of a conversation isn't automatically classified as a greeting when inputted later. If I say 'Hello' at the outset, it signifies I wish to address the digital assistant – this constitutes a digital dialogue process. If I say 'Hello' mid-conversation, it may convey gratitude. Thus, our primary objective is to comprehend the consumer's intent.

For instance, having already taken out life insurance, we can now proceed with a quote. You can now view several pricing options, approximately three life insurance products: whole life, short-term, and long-term policies, such as a 27-year term. This process typically requires around 30 days for a quote, but this product enables us to engage with customers much more swiftly, helping them purchase products more efficiently.

Simultaneously, the same product accelerates assessment procedures. For any insurance policy, all relevant data is systematically gathered. Consequently, every conversation with the client is meticulously recorded. By reviewing life insurance quotations, we can identify solutions and complete forms automatically—without human intervention.