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AI in Fintech – Current Applications and Use Cases

In their recent Worldwide Spending on Cognitive and Artificial Intelligence Systems report, IDC estimated that banking investments in cognitive and AI systems in 2018 might total around $4.0 billion.

Many AI vendors claim to offer software for numerous finance use cases, such as credit scoring, insurance underwriting, debt collection, fraud detection, and more recently, financial regulations, or so-called regtech.

The company offers a software called Yseop Compose, which it claims can help banks and financial institutions automate report generation using natural language generation, or NLG. Natural language generation is the process of generating natural language text from data which is usually stored in the form of machine-readable databases.

Yseop claims users can install their software on enterprise computers and input it with financial data, data for risk analysis, compliance data, and other kinds of internal business data.

team of subject-matter experts from the client company works alongside Yseop developers to help organize, classify, and understand the context behind the data that is fed to the software.

Yseop Rules allows the client to create a dashboard listing the relevant factors that might affect this metric, such as the directional change in asset value and a slider for the strength and duration of the change.

In the example considered this would be a narrative report on the deposits, savings, equity and other factors related to the total assets held as shown in the figure below: Yseop provides an online demo for their natural language generation software.

The company offers a software called Quill, which it claims can help financial institutions generate narrative reports from structured data, such as sales records, using natural language generation.

According to Financial Crimes Enforcement Network (FinCEN) regulations, banks and financial institutions are required to file a Suspicious Activity Report (SAR) with the organization upon detecting any suspicious transactions or money laundering event within 90 days of such activity.

The company offers a solution called Kai Score, which it claims can help investment and equity firms predict the performance of securities in a portfolio using predictive analytics, big data, and machine learning.

Kavout claims their software can help investment and equity firms analyze large amounts of data, including SEC filings, technical indicators, price patterns, news, blogs, analyst reports, and social media feeds.

For example, the screengrab below shows the predicted Kai scores for a number of different portfolios of stocks in the S&P500: In the first 4 minutes of the video below, CEO Alex Lu, demonstrates how the Kai Score works: Kavout does not make available any case studies reporting success with their software.

This enables banks to offer several services through the chat interface, such as product discovery, allowing customers to apply for loans, and answering customer queries on transaction and account details.

Below is a short 3-minute video demonstrating how KAI works in a collaborative project with Mastercard: Kasisto claims to have helped JP Morgan’s treasury services division offer clients a chatbot that can answer customer queries.

JP Morgan deployed the chatbot solution to help its customers navigate through their website’s 1,200 pages in order to accomplish tasks such as sending wire transfers or exporting data from multiple accounts to determine balances.

The company offers a software called Assist, which they claim can help banks and financial institutions create conversational agents aimed at improving customer experiences using NLP.

Based on our research, business intelligence and analytics seems to be the business function with the most traction for AI in finance currently, with applications such as automation of report generation, predictive analytics, and text and data analysis.

In the customer service horizontal, AI is actively being used by large investment banks and finance companies to handle the massive customer query and feedback volumes they receive.

In this sense, companies looking to implement AI for automating business functions might need to be prepared for a three to six month integration period along with the challenges of hiring good Ai talent and the high costs involved in AI projects.

JAIR is published byAI Access Foundation,a nonprofit public charity whose purpose is to facilitate the dissemination of scientific results in artificial intelligence.

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