Can financial services chatbots fulfill their potential? It all depends on data.
Bots are going ballistic. Transparency Market Research predicts the global chatbot market to be worth $7.9 billion by 2024. This year, financial services bots will increasingly service banking, trading and insurance. 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, with over three quarters of surveyed financial institutions viewing chatbots as a near-future commercial solution.
But how can chatbots be effective? How will they engage audiences and transform Personal Financial Management into intuitive personalised digital assistantship? Jake Tyler the CEO of Finn.ai develops white-label personal banking chatbots for financial institutions.
He argues bots have found an audience by servicing the immediacy demanded by today's customer.
“If banks want to attract Millennials they will need to be where they are, on instant messaging platforms. Equally as important, chatbots are a way for banks to communicate with this generation in a way they are familiar with, by texting.” he states.
Finn.ai chatbot app
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, enabling account holders to check accounts, track spending and review purchases.
Therefore bots must be embeded into messaging platforms to speak to their customers. But how can they understand every end-user's financial position and make predictive assessments? A crucial underlying data source for financial bots is based on account
aggregation technology but not every bot uses this. It’s this technology that 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. The best bots are only as good as
the aggregation technology supporting them.
Without data aggregation able to collate different elements of an end-user’s financial footprint, a chatbot will only interrogate one data-set. Similarly, the limits of Personal Financial Management (PFM) was exposed when legacy banks were initially reluctant
to share their customers’ data with each other, only allowing the PFM tools to operate on their own internal data set. Using multiple bank-connectors, Account Aggregation Fintech companies are able to aggregate from multiple banks in multiple countries, securely
aggregating a plethora of data sources, but not every technology provider has this capability. Without aggregation technology, chatbots will fall short in supplying the intuitive, flexible Personal Assistant service now demanded by end-users.
In addition, chatbots will fail to deliver accurate results and advice to customers without quality categorisation technology converting raw transactional data into actionable and meaningful data. This creates the personalised end-user data on which the
bot depends. This technology relies on end-users reporting in-accuracies in the transaction data: the AI learns from its mistakes, creating the personalised data for intelligent Personal Assistantship and enabling predictive behavioral services for Financial
Institutions and FinTechs.
Chatbots in financial services do have tremendous potential, but that potential will be dictated by the data they are built upon. Categorisation and aggregation technology must be fully supported to give bots the foundation they need to personally benefit
each and every customer.