Industry Trends in Financial Analytics

The Era of Augmented Analytics

Moving away from descriptive analytics and toward predictive and prescriptive analytics. Now utilising “Big Data” beyond the general ledger.

Reasons for Using Change Models

Provide Structure to manage complex transitions, help leaders predict employee reactions, enable targeted support strategies, improve adoptions and reduce resistance

Algorithmic Forecasting (AI and Machine Learning in Forecasting)

Using ML to identify non-linear patterns that humans or standard spreadsheets miss

Sentiment Analysis (AI and Machine Learning in Forecasting)

Using Natural Language Processing to analyse CEO speeches, news reports, and earnings calls to predict stock movement.

Anomaly Detection (AI and Machine Learning in Forecasting)

AI-driven tools that identify fraud or accounting errors in real-time rather than during month-end audits

Robotic Process Automation (RPA) (RPA and the “Zero-Day” Close)

Automating high-volume, repetitive tasks like data entry, bank reconciliations, and report distribution

The Continuous Close (RPA and the “Zero-Day” Close)

Shifting from a stressful “month-end” close to a real-time financial snapshot available any day of the month

Efficiency gains (RPA and the “Zero-Day” Close)

Reducing manual data manipulation by up to 70%, allowing analysts to focus on intepretation rather than preparation

The End of Static Reports (Real-Time Self-Service BI)

Moving from 50-page PDFs to interactive Power BI or Tableau dashboards.

Self-Service Access (Real-Time Self-Service BI)

Enabling non-fn