Requirement Analysis

Analyze identify and finalize the KPIs, measures their formulae calculations required for the decision making process. This is followed by identification of the data elements and the system of record for each of the use cases identified  1) what systems/apps are needed 2) data location (on-prem, cloud) 3) SaaS modules & 4) Individual Excel, CSV or any other external data sources. The overall objective is to ensure that identified requirements in this exercise can be delivered with the accurate data points, formulas, relationships and the hierarchies. 

 

Data to Decisions: DATA

Data Analysis & Management                                                           

In data analysis and management section we perform data analysis, data verification, data validation identify the system of record, and finally perform the data cleanliness and ongoing data governance. The most important aspect is to ensure that data is clean and stays clean, the outcomes are only as good as the input data. If data is outdated, dirty or unmanageable, then it cannot be used. Imagine looking for loan origination volumes and finding that loan officer, branch information is outdated or some of the loan origination dates are “01/01/2000”. No amount of technology investments - AI, ML or neural networks will produce results if we do not clean data.  Data transformations are performed to get to ongoing pristine data.

 

Data to Decisions: INSIGHTS

Analytics                                                                                                      

Analytics is vehicle to provide basic insights into the data and facts to help in the decision making,Top 5, bottom 5 or data over time across all the data points – channel, region, branch, loan officer. Additionally you drill thru, drill down and also perform cross filtration on the desired data points. Analytics gives the capability to analyze the increase/decrease in volumes, $, in the desired visualization. Objective of the analytics is to provide 1) Accountability, 2) Insights, 3) Accurate reflection of reality and 4) Decision Capability. The desired goal is provide users with the deep insights – using Paretto rule, Segmentation (Grouping and ranking), Cohort analysis, exception management & proactive alerts

 

Data to Decisions: ACTIONS 

Advanced Analytics/Models

 Advanced Analytics uses all of the latest technologies(R and Python) to model the data - Artificial Intelligence, Machine Learning, Deep Learning and neural network algorithms. The information architecture we deploy, is to train and enable AI models and machine learning.

Humans can manage a few hundred keywords at best together. AI can manage hundreds of thousands, words, images and patterns and help predict the outcomes, this provides us the capability of augmenting the data.

 

Our offering provides standard insights on run time which offer trends and patterns in the data. Additionally the entire offering is integrated with Natural query Language – this allows business people to generate queries, explore data and receive and act on insights in natural language using voice or text.

 

Additional AI algorithms include, Influencers and Segmentation – what influences the loan amount to be higher or lower, Outliers – scoring each loan on certain characteristics and identifying the outliers, Correlation – predicting relationship (strong, moderate, weak and no relation) between multiple data point, lastly ability to integrate with R and Python code and APIs.

 

Resulting in QUALITY business decisions

Data to Decisions

Use of AI and ML trained models to decide results in quality decisions made much faster, based on real data.