Bots are going ballistic. Transparency Market Research predicts the global chatbot
market to be worth nearly $1 billion by 2024. And according to bot specialists Personetics, there will be a surge in chatbot
companies considering entry to the conversational financial bot space in the next 12 months.
But how can chatbots become effective? How will they engage audiences and transform personal financial management (PFM) into intuitive personalised digital assistance?
The appeal of messaging apps
Millennial tastes explain Facebook’s bot appeal. Facebook Messenger has over a billion monthly active users and more than 30,000 chatbots. Facebook beneficiaries include MasterCard, which will use artificial intelligence to communicate with customers through text messaging and speech. Therefore, bots must be embedded into messaging platforms to speak to their customers. But how can they understand every end user’s financial position and make predictive assessments?
Categorisation and aggregation technology
A crucial underlying data source for financial bots is based on account aggregation technology.
This allows the personal finance chatbot
to access all of an end user’s financial accounts and return a comprehensive picture of his or her finances. However, not every bot uses this. The best bots are only as good as the aggregation technology
Without data aggregation
or the ability to collate different elements of a user’s financial footprint, a chatbot
will only interrogate one data set. Similarly, the limits of PFM were exposed when legacy banks were reluctant to share their customers’ data with each other, only allowing PFM tools to operate on their own internal data set. Using multiple bank connectors, fintech companies are able to aggregate from multiple banks in multiple countries, securely collecting a plethora of data sources.
This is an excerpt from an article published on TechInAsia. You can read the full story here