Revenue Protection South AmericaA machine learning approach to reducenon-technical losses

Mario Namtao Shianti Larcher

Data Competence Center

Global Digital Solutions

TopicsOur journey for a better identification of frauds and malfunctions

Introduction

Current modelling approach

Architectural details

Results

EnelWho we are

71 million end

users around the

world

Over 70,000

people in 34

countries

Thermal

capacity

46.6 GW

Renewable

capacity

42.5 GW

Revenue Protection

4

What do we mean by revenue protection?

In the utility sector is very important tocarry out targeted field inspections inorder tomaximize energy recovery.

Revenue Protection is a global project withthe goal of identifying frauds andmalfunctions using advanced analytics.

DataWe live in a Big Data world

Geography

Consumption

Meter

Contract

HistoricalEvents &Inspections

Modelling PipelineFrom raw data to a score

Feature EngineeringInject our domain knowledge into the model

Consumption Localization of the drop in consumption

Estimation of the consumption lost

Consumption statistics (mean, standard deviation, etc.)

Meter Ease of tampering

Malfunction rate

Contract Behavior based on the tariff type

Behavior based on the industry sector code

Geography Latitude

Longitude

Area hit rate

Historical Events & Inspections Suspension history

Previous inspections results

Meter / customer changes

Feature EngineeringExample: Localization of the drop in consumption

BiggestDrop: The biggest

drop in absolute value

Greater20Drop: Last drop

greater than 20%

MinMaxDrop: Minimax

algorithm, locate the drop

using game theory

BiggestDrop

MinMaxDrop

Greater20Drop

PlayerMax

PlayerMin

Probabilistic decompositionHow we break the problem

E(Energy | X, Fraud or Malf) P(Fraud or Malf | X)

E(Energy | X)

ClassificationRegression

“If linear regression was a Toyota CamryFiat 500, then Gradient Boosting would be a UH-60 Blackhawk Helicopter” Ben Gorman

Gradient boostingBeyond linear models

Left: Example of partial dependence plot,

many features have a clear non-linear relation

with the target

Sampling biasWe have the wrong data for our goal

𝑷 𝑭𝒓𝒂𝒖𝒅 𝒐𝒓 𝑴𝒂𝒍𝒇 𝑿= 𝑷 𝑭𝒓𝒂𝒖𝒅 𝒐𝒓𝑴𝒂𝒍𝒇 𝑿, 𝑰𝒏𝒔𝒑 ∗ 𝑷(𝑰𝒏𝒔𝒑|𝑿)+ 𝑷 𝑭𝒓𝒂𝒖𝒅 𝒐𝒓𝑴𝒂𝒍𝒇 𝑿,𝑵𝒐𝒕𝑰𝒏𝒔𝒑 ∗ (𝟏 − 𝑷 𝑰𝒏𝒔𝒑 𝑿 )

What we would

like to estimate What we are

estimating

What we cannot

estimate

General Overview of Data Flow

12

We’re using a Big Data Architecture

ED Chile

Data Lake Market

Local legacy systems +

new Enel systems

ED Peru

Codensa

Edesur

ED Rio

ED Ceara

CELG

Build up of

complex

variables &

machine

learning

predictive

models

Data Lake NCO I&N (7 Worker Nodes) computation

nodes

Bases

Nightly

refreshMonthly

refresh

Score

120 final

tablesHundreds

of tables

21 extractors to extract,

clean and organize data

ResultsThe impact of our Machine Learning solution

Preliminary results

(Oct 2017-Sep 2018)

+0%

Improvement in theTPE rate

with the new ML approach

> +500%

CODENSA

+700%

Even compared with

previous sophisticated

approaches, our new

100% Machine Learning

solution appear to be a

winning strategyED CHILE

+50%

ED CELG

+300%

ED RIO

+70%

ED CEARA

+100%

ED ARGENTINA

+80%

14

THANK YOU!