MobiDev Explains Key Features of AI-Powered Financial Assistant App

By AIT News Desk On Jun 15, 2022

The line between an ordinary financial management app and a powerful financial assistant lies in the use of artificial intelligence. According to Mordor Intelligence, the global AI in the fintech market is expected to reach $26.67 billion by 2026. MobiDev took a look at some advanced features based on this technology.

BIOMETRIC AUTHENTICATION

Biometric authentication technology is considered one of the most reliable ways to protect data. It can be implemented in the way of facial recognition, iris scanning, fingerprint identification, or voice verification. However, each option has its implementation features. For example, creating iris scanning on mobile and desktop is not possible without special hardware, since the resolution of conventional cameras is not enough.

CONVERSATIONAL ENGINE

Based on Natural language processing (NLP) and Natural Language Understanding (NLU) technology, the conversational engine enables smooth communication between a financial app and its users. AI voice assistants use a device’s microphone to receive voice requests. First of all, they need to recognize a command (a wakeword) that helps wake up the device, since virtual assistants are usually passively listening. Further, after triggering, voice recognition, voice analysis and language processing go to work and the magic happens.

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Personal finance assistant apps can detect user behavior patterns as well as make predictions on future users’ income and expenses. This happens thanks to statistics and modeling techniques. Predictions are made based on historical data of account transactions powered by machine learning algorithms. Predictive analytics will let users plan for the future and tell them how best to achieve their financial goals acting like a real financial advisor.

RECEIPT RECOGNITION

To provide recognition of the receipt, the system extracts the text from the photo of your receipt and analyzes it to determine which data corresponds to the categories embedded in the system. After that, the module analyzes existing spending categories and looks for suitable ones in order to add information from a new receipt.

The main challenge is that receipts can be represented in different formats, which complicates the analysis of information and its further distribution. This is where you need effective machine learning models.

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