The document discusses how Uklon, a ride-hailing company, has implemented machine learning and AI across various parts of their business to improve operations and the customer experience, including surge pricing prediction models, fraud detection systems, feedback automation, and marketing analytics. It provides examples of how ML has helped increase completion rates, reduce wait times and costs, and prevent fraud losses for Uklon.

1. Transforming your
business with ML
The way we cook it.

3. Nice to e-meet you!
Vitalii
Diatlenko
Co-founder & CTO
vitaliy.diatlenko
UKLON007

4. Uklon 2009

5. 4
Uklon 2009

6. 27
cities
10,5mln
app installations
200k
drivers
>3mln
monthly rides
Uklon in Numbers

7. 100 Employees in IT
8 Cross-functional teams
> 400 Employees in company
Uklon Team

8. Rider Freedom
• Fixed rates
• Variety of products
• Variety of additional options
• Price control

9. Driver Freedom
• Low commission
• Suitable for day2day rides
• Suitable for professional drivers
• Filters

10. Driver
Rider

11. Driver
Rider

12. Unique competitive advantages
lead to HIGH EXPECTATIONS
FROM THE PRODUCT
3 1
2

13. Driver
Driver
Driver
Driver
Dispatching office
1

14. # NPS falls
riders simply do not understand
what price is required
#
1
Not enough drivers
during rush hours
#
2
Waiting time has increased
due to high price expectations by drivers
Scaling issues

15. We should offer the
most suitable price
right here and right now
"
"
SURGE PRICING Factors to take into account:
• reconfigurable filters
• different classes of products, change of cost
• acceptance of rides by drivers
• ride requests from distant locations
• lack of drivers for proper dispatching
AI & ML Challenge for Uklon
> 90%
Completion rate
<7min
Waiting time
per hour
Driver earnings

16. Microsoft AI
2017–2018
• +5-7% p2c
• multifactor analysis
Attempts to build Surge Pricing model
• simple math only
• p2c up 80%
• geometry issues
• price guessing
KISS
2016–2017
SurgeML
2018–now
• time lags
• driver’s filters
• ride acceptance rate
• historical data
• predictions

17. 1. historical datasets
2. switched from S2 polygons to H3
3. short and long-term forecasts
4. market expectations — filters
5. drop-off forecasts
6. weather conditions
7. traffic conditions
h3_7_p2a_15min_30_sec
placed_roll_sum_10_h3_6
drivers_filter_offer_h3_6
placed_roll_sum_15
h3_7_p2a_10min_30_sec
placed_roll_sum_5_h3_7
H3_6_p2a_5min
H3_6_p2a_10min
Ewm_drivers_filter_offer_0.9_h3_7
Ewm_drivers_filter_fast_search_0.9_h3_7
placed120s_roll_sum_15
Ewm_completed_0_5_0.1
Drivers_filter_fast_search_h3_6
H3_7_p2a_15min_120_sec
Drivers_filter_offer_h3_7
placed30s
0 2 4 6 8 10
Uklon SurgeML iterations

18. • MAE 11%
• <30sec to accept 75% of rides
• normal surge distribution
• average check +10%
• resource consumption -20%
• NPS = 74%
rides are completed
93%
Uklon SurgeML Results
Lviv

19. 24/7 rider care
Driver care
Quality control
Customer service departments

20. FeedBacks dilemma
• 10min per feedback processing
• business hours only
• max 100 feedbacks per day
• operator is a subjective human
"
1
2 of 10 rides rated
human operator
efficiency:
Dilemma arises when we need
to process 1 mln rides =
=90operators
x10

21. Anything that can be
automated must be automated.
"
ES

22. DATA SOURCES
Ride rating Predefined options Frequency Ticket numbers

23. 70%
well classified data
Ride rating Predefined options Frequency Ticket numbers

24. • +3mln rides are processed
• Automated promotions
• Accounts blocking
• Cross-department
notifications
• Police notifications in
emergency situations
• Ride rating
• Predefined options
• Frequency
• Ticket numbers
Not only to count but to understand.
Supervised
learning
NLP
Retrain
and Adopt
90%
automated
scenarios
Data
sources

25. Finance & Marketing: Risk modeling
Weak elements exposed to fraud:
• Multiple payment providers
• Google pay
• Apple pay
• Instant payments for drivers
• Free rides for riders
• Promotion campaigns with discounts
5%
of revenue
worldwide organizations
lose due to the fraud*
*https://www.accountingtoday.com/news/organizations-lose-5-of-revenue-to-fraud-every-year

26. Finance & Marketing: Risk modeling
$10k+
overnight
Uklon lost because of the fraud
5%
of revenue
worldwide organizations
lose due to the fraud*
*https://www.accountingtoday.com/news/organizations-lose-5-of-revenue-to-fraud-every-year
Weak elements exposed to fraud:
• Multiple payment providers
• Google pay
• Apple pay
• Instant payments for drivers
• Free rides for riders
• Promotion campaigns with discounts

27. Uklon Antifraud system results
10
25
51
38
56 57
106
58
76
126
159
177
155
184
121
106 100
118
64 69
106
135
168
124 128 119
193
141 145 141
171
73
127
141
173
156
139 129 125
287
0
50
100
150
200
250
300
350
Antifraud preventions for small city — 2021 (weekly)
80%
of fraud operations blocked
90%
of chargebacks prevented

28. ML in
MARKETING
with 80% probability we know
if the rider will continue to use Uklon

29. Rider & Driver:
Pickup points

30. Rider & Driver:
Pickup points

31. Rider:
Drop Off
Recommendations

32. Rider:
Drop Off
Recommendations

33. up to 128%
the performance of the product
analytics could be improved
by implementing AI & ML*
product improvements
process automation
detect anomalies
discover patterns
and connections
ML or just hype
* McKinsey Global Institute Analysis

34. Lessons learned
KISS
Do not reinvent
wheels
Data
Fuel for
modeling
SaaS
Prefer a service
where possible
Team
Start with people
not models
Costs
Be ready
to invest

35. CTO and co-founder of Uklon
Vitalii Diatlenko