Democratizing AI in Internal AuditIndumon Das-Beinex Consulting

Need of the Hour

College students allegedly scammed Apple out of nearly $1M in iPhone replacements. In 2019

A Man Scammed Over $100 Million Out Of Google And Facebook With Fake Invoices. In 2019

Democratizing AI

DecisionScienceExpert

World of AI

S.S* Advanced AnalyticsSolutions

IT Operations

Audit

S.S*AdvancedAnalytics

S.S*Visual

Analytics

World of AIDecisionScienceTeam

IT operations

Audit

S.S*- Self Service

Transforming Every Phase of IA Cycle

Risk Assessment Audit Planning Audit Execution Audit Reporting Continuous Monitoring

Risk identification

Review of material transactions

Significant account activity analysis

Embed analytics in existing risk assessment process

Audit scoping

Perform analytics

Analytic testing of other significant processes

Customize data requests

Develop continuous control monitoring Scenarios

Automate the end-to-end Audit workflows

Predict exceptions & risks before occurrence

Create audit analytics visualizations (i.e., dashboards, scorecard)

Compare with known benchmarks

Turn data into insight

Resource Planning

Develop audit plan

Research current available data & information

Coordinate lead time to execute

Machine Learning

Predictive Analytics

Visual Analytics

Predictive Analytics

Visual Analytics

AI

Machine Learning

Descriptive Analytics

Visual Analytics

Augmented Analytics

NLP, NLG

Visual Analytics

Augmented Analytics

NLP, NLG

Visual Analytics

Machine Learning

Tech

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Common Bottlenecks across IA functions

Data completenessFailure to confirm data completeness though reconciliationsFailure to challenge the business to enhance data capture

Data accessibility and availabilityLack of visibility of the existence of enterprise data assets Data obtained in an ad-hoc fashion without creating a foundation for continuous audit

Data qualityLimited understanding of dataPoor quality data affects validity of results Failure to challenge the business to improve data quality controls

What Makes Self Service AI & ML Different

N o c o d i n gDrag & drop tools using an intuitive user

interface to prep, blend, and analyze data

U n l o c k a l l y o u r d a t aSecurely connect business users to all data

regardless of source or data type

E l i m i n a t e s i l o sBridge the gap between disparate teams and

departments by collaborating in a secure,

centralized analytic platform

A n a l y t i c g o v e r n a n c eEnsure data quality by providing transparent

data management and auditability to data

sources, authors and transformation

E n t e r p r i s e s c a l a b i l i t yScale analytics to service users in the

systems/technologies they depend on

R e p e a t a b l e w o r k f l o wAutomate time-consuming, manual data tasks,

and adjust analytic queries easily

Financial Fraud Detection Using Machine Learning Techniques

Overview

► Data Transformation: Consolidating and transforming the data from different data sources

► Rule Based Anomaly Detection: Defining the commonanomalies expected and defining conditions/rules todetect these conditions in the dataset

► Cognitive Anomaly Detection: Implementing neural network algorithm to find out the similarity in the dataset .The neural network works on a feedback service, which takes up the frauds detected by algorithm to train the model and identify similar fraud in the future datasets by looking at the underlying layer connection.

► Significantly reduce the human interaction and thereby errors associated with auditing process

► Enhanced automation that leads to fewer manual reviews

► Increased accuracy to identifying good orders versus fraudulent orders

► Allows data to be better classified and trends to be identified quickly

► Take back hidden insights and better understanding of data

Corporate malfeasance is one of the most roadblocks in the progressive growth of any institutions. Financial frauds are often hard to detect and even analyzing the same is a tedious process often requiring many human hours. The emergence of the machine learning technique is one of the technological boons which when applied correctly can accurately predict the frauds instantaneously .

Business Case

Solution Overview Impact

Overall Architecture

Data Preparation & Consolidation

Extracting data from different datasources

Consolidating the extracted files into amaster dataset using Alteryx

Recurring Data Analytics

Creating predefined workflows inAlteryx to automate the commonlyrecurring analysis and visualizing thesame on tableau

Cognitive Anomalies Detection

Creating algorithms to identify theanomalies/predict changes in the dataand to train the algorithms to refineitself based on self learning techniques End Users

Data Auditors

Mobile| Tablet |Internet Browser

Automated Alteryx Workflows

Tableau Dashboard

Cognitive Algorithms

Tableau Dashboard

Alteryx Environment

Alteryx EnvironmentAlteryx Environment

Machine Learning Process With Random Forest

Random Forest ModelF M

Variable Selection Based On Importance1) Invoice Amount 2) Net Payment 3) Avg. Amount 4) Payment Due Bucket 5) Invoice Age (in Days) 6) Created By 7) Balance Due 8) Paid Status 9) Approval Amount

Threshold

Here, we have employed the power of supervised learning ‘Random Forest’ modelling technique to fine tune our methodology to predict whether a new transaction made will be fraudulent or not. "Random Forest" is basically multiple decision trees which merges them together to get a more accurate and stable prediction by looking at the overall mean value of the decision trees built.

Live-DEMO

Augmented Analytics as an approach that automates insights using machine learning and natural language generation.

Live - DEMO

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