This document discusses various applications of artificial intelligence in the telecom sector. It begins with an overview of the basics of AI and its present status in telecom. It then discusses using AI to improve network monitoring through drone usage and improve customer experience metrics like quality of experience. Other topics discussed include using AI for customer care applications, disaster management, and detecting fraudulent SIM subscribers through facial recognition. The document emphasizes that AI can help telecom companies deliver better customer experience, introduce new products, and reduce costs through automation.

2. Topics of Discussion:
Basics of AI - Present status of AI in Telecom Sector
Network Vs Subscriber View- QoE
AI + Drone Cell Tower Monitoring and EMF Audits
Customer Care Applications and Portals - Call Drop, QoS
analysis
ASTR (अस्त्र) and its impact

3. Traditional Programming vs ML, DL &AI
Computer
Data
Program
Output
Traditional
Programming
Computer
Data
Output
Program
ML, DL and
AI

4. Why Artificial Intelligence?
 Present day and Future Considerations –Data Generation and its 5 V’s -
Velocity, Volume, Value, Variety and Veracity – beyond human brain’s
processing powers.
 Technological advances – more data processing powers and storage
 Will provide capabilities beyond problem solving

5. AI in telecom sector-Present situation
• Summary Report -76 TSPs, operating in 51 countries.
• 18% have adopted AI for their products and services.

6. AI in telecom sector – Driving factors
Major Driving Force for AI in your organization?
For delivering betters
Customer Experience
For selling new
products to existing
Customers
Reducing OpEx by
using automation

7. Network Versus vs Customer View: QoE
•
How TSPs
Manage ?
Customers’
View
• It takes me around 10s to open a website
page.
• I cannot receive my email
• It takes a long time to connect to customer
care executive.
• My phone is showing the network but I
cannot access internet.
How does TSP do Network Management
• KPIs Statistics
• Dashboard
• Alarm identification – equipment's
monitoring The big question
is: how to make
customer QoS
experience an
integral part of
NMS?

8. AI+Drone enabled Cell Tower Monitoring
• Verizon and AT&T - for monitoring their cell sites
• Tower inspection algorithms for checking assets - 6,000 videos, > 1 million cell
tower photos as datasets – 200 elements identified
• Telecom tower sites located in remote areas are usually left unattended.
• Fuel and expensive telecom equipment
on these sites - susceptible to theft
and fiddling.
• Drones provide a bird’s view eye
• Can be very effective during
disaster management.
• Helpful for inspecting surroundings

9. Lat/long of mobile towers- available on Tarang Sanchar portal.
AI + Drone based monitoring of mobile towers - lat/long, radius details to
drone
EMF emission data of telecom towers may be collected with these AI
enabled drones
Like Air Quality Index - Safe, Critical, Bad, hazardous to eradicate fear of
radiations levels among Public - Real time data may be fed to LED
displays
Data pushed to a centralized server for mapping emission levels to different
categories for EMF Compliance purposes
AI for EMF emissions audits and public awareness
of EMF emission

10. Present setup for grievances redressal
Access
• Contact to any TSP’s Customer Care is – through Call or email
or SMS
Time Delay
• Time taken by any subscriber to reach to customer care
executive after wading through IVRS is more than 60s
• Unnecessary wastage of resources, public telecom network
• Poor QoS experienced by subscriber even during lodging
complaints/grievances
Chatbot
• Some TSPs websites have implemented interactive chatbots
• TSP downloads its APP on user mobile device

11. AI enabled Chatbots & self-assessment APPs
TSP App may have an inbuilt
button of Health Check wherein
following parameters will be
automatically checked for
identifying issue:
i. Type of network and technology
ii. Signal strength
iii. Data speed – uplink and downlink
iv. Location
Datasets of all subscribers:
•To be used for finding subscriber
receiving worst service;
• A benchmark may be set as a
threshold value.
• If that criteria is not fulfilled, the App
will make an automatic call to call
centre where the grievance may be
directly routed to the customer care
executive.
The datasets may be used for machine learning for taking better decisions.

12. AI for Disaster Management
IMD issues warnings and alerts for natural hazards & disasters
Common Alert Protocol (CAP), an indigenous application
designed by C-DOT, is being used for sending alert SMSs to the
subscribers present in affected areas of expected disaster
Number of people dialing the helpline codes increases
exponentially – results in network congestion
CAP and EWS + Location Based System (CQAS) - extract
information about approx. count of people in area - evacuation

13. NextGen Digital Intelligence Tool
for
ASTR (अस्त्र ) : AI and Facial Recognition powered Solution
for Telecom SIM Subscriber VeRification
Detecting and
weeding out
fake/forged SIMs
before used for
cyber crime and
frauds
Prevention
of cyber
crimes and
frauds
Catalyzing
the
investigation
of cyber
crimes
By:- Naveen Jakhar, ITS
ADG (AI & DIU)
DoT HQ

14. The Menace of Cyber Frauds and Crimes

15. Fake SIMs and Problem of Cyber Frauds, Crimes
> 11 lakh complaints on
NCRP in last 3 years
Huge Vulnerable Attack
Surface of 1.16 billion
telecom subscribers
50000 Cyber-crimes
cases in 2020,
52974 cases in 2021
Pendency of cases > 70%
by end of 2020
Fake SIMs - Direct Threat to Digital Economy and Digital Inclusion
30142 cases of
financial frauds ~ 60%
India lost Rs 1.25 Lakh
crores due to cyber crimes
in last 3 years.
Major cyber crime
syndicates - from
Jamtara and Mewat.
Direct threat to national
security, public law & order
and safety
> 2.5 lakh SIMs
involved in cyber crimes

16. Nexus b/w Cyber Crimes and Fake SIMs
Why cyber crime
investigation is
complex ?
• Anonymity, Untraceability,
Trans border syndicates
•70% cases pending
investigation
• <5% conviction rate
Fake SIMs in financial frauds,
cyber crimes, extortion, dacoity,
kidnapping, hoax, threat calls,
advanced crimes like sextortion

17. Cyber crime hotspots in India
Mewat - Haryana
Jamtara -Bihar Jharkhand
Jammu Kashmir
North Eastern States
Rajasthan
West Bengal & Kolkata
Odisha
Assam
Punjab
Delhi NCR
Gujarat
Uttar Pradesh

18. SIM Subscription Process in India
Paper Digital -KYC E-KYC using
Aadhaar
SIMs – 18 crore,
13.5% of total
SIMs – 63 crore,
47% of total
SIMs – 53 crore,
39.5% of total
Total SIMs in India as on 31.12.2022 – 134 crores
Photo,
KYC – Name,
DOB, PoI/PoA,
Tele-verification
Live Image
Live Photo,
KYC – Name,
DOB, PoI/PoA,
Live Image

19. Existing SIM Subscriber Verification - Limitations
No centralized SIM Database, TSPs - individual database
> 30 PoI/PoA documents – for connection
No online mechanism for PoI/PoA verification for SIM activation
Text Based Analysis by LEAs and DoT LSA field units
Creation of fake/forged PoI and PoA by fraudsters
0.1% and 0.2% sample CAFs audit by DoT LSA units

20. Existing SIM Subscriber Verification - Limitations
Consequence: Mostly SIMs found compliant in conventional audit/investigation for individual
case –
so even after being reported by citizens / LEAs, cyber crime gangs and their SIMs remain
functional
LEA reports to
DoT
Mob Num
involved in
cyber crime -
xxxxxx9380
DoT carries out
CAF compliance
for this number.
Details on CAF
match with
PoI/PoA used
Mobile
Number
Found
Compliant
Expertise level of the fraudsters in creation of fake Proof of Identity/Address documents
Can never be detected by human beings by analyzing single case in isolation

21. KYC DETAILS: NAME, DOB, ADDRESS
AADHAAR NO.
PHOTOGRAPH
PoI - VALID AADHAAR DOCUMENT

22. ASTR: NextGen Digital Intelligence Tool
Live Image
Live Image
Fake PoI/PoA-
Aadhaar
Fake PoI/PoA-
Aadhaar
Name - Atul Name - Raju
Fraudulent subscribers –
SAME Face but different
names other details
68 facial landmarks
detected
68 facial landmarks
detected
Cosine product
comparison, face
matches at
99.25%
Name Similarity Check, matches
38% - different Names

23. ASTR: NextGen Digital Intelligence Tool
Fraudulent subscribers –
SAME Face but different
names other details
Individual
Subscribers >
prescribed count
Face comparison with very high grade
accuracy
ASTR – agnostic to PoI/PoA
Naveen
Navien
Norman
Nakeem
Quantum jump from 0.1% to 100% SIM
verification – 1000 times

24. Presenting the Power of ASTR: 7 SIMs, 3 Names
Male and Female – One Person in Mewat, Haryana
Sample Case -1

25. ASTR: Impact in Cybercrime hotspots
Sample Case -2
213 SIMs across
different TSPs
100 Names
100 Forged PoI/PoA
ASTR detects Fraud
ONE PERSON

26. ASTR: Impact in
Cybercrime hotspots
52 SIMs, 29 Names
Male and Female
29 Forged PoI/PoA
ASTR detects Fraud
- One Person
Sample Case -3

27. 2655735
15748
1
10
100
1000
10000
100000
1000000
10000000
1
NUMBER
OF
CONNECTIONS
ALL TSPs
FACE RECOGNITION BASED SIM
ANALYSIS RESULTS
Total Number of suspected fake/forged SIMs detected
Total Number of more than prescribed SIMs disconnected

28. ASTR – for Breaking the Chain
of Cyber Crimes and Frauds SIMs on
fake/forged
Docs or in 3rd
parties Name
Bank accounts,
social media
handles-
Whatsapp,
Facebook,
Telegram Payment
APPs
Vishing,
Phishing,
SMiShing
Attack for
Financial
Fraud
Instant Money
Withdrawal by
Fraudsters
Un
Traceability
and
Anonymity in
SIMs during
investigation
Breaking the
Chain
Cyber
Crimes/Frauds
Cycle
A tool to curb cyber
crimes/frauds
Pro active Analysis and
Communication
Intelligence using ASTR
-
Detecting and weeding
out the fraudulent SIMs
even before they are
used to carry out any
cyber crime/fraud

29. AI for Subscriber On-boarding
For checking whether the Point of Sale are working as per
instructions
Sudden rise in SIM sale in an area, Ghost PoS
Inputs to the Activation Officer before SIM Activation
In Subscriber on-boarding APP – live human face detection, de-
duplication of subscriber using KYC details and image

30. “I believe this artificial intelligence is going to be our partner. If we
misuse it, it will be a risk. If we use it right, it can be our partner.”
--Mashayoshi Son
Thank You !!
Your queries please.