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Data-driven cases for business
amplification in modern digital
banking
Nikita Pustovoytov
Head of Data Science
BCS Bank
Data Science Team*
Retail
NeoBank
BCS Structure
Wealth Management
*business-oriented department
Marketplace is huge Low approval rate Uncertainty Distrust
average approval rate in
banks and microfinance
institutions re...
- innovative offer aggregator
Odobrim.ru — the first credit marketplace got banking license
Started at Q’4 2018 service su...
Basic economy
• Organic (zero-cost, but unscalable) -
too small because of a young and unknown project
• Performance (scal...
Data Science – steroids for business
6
Which
audience to
acquire?
Which new
ecosystems do
customers really
need?
How to in...
leads
Marketing
platform
KYTA – Know Your Target Audience
7
Initiatives and their influence on sales
Ad Media Analysis
Seg...
Key challenges for Data Science and Odobrim
Overall CR (traffic issue) < 1%
Latency btw claim and issue notification – up ...
9
KYTA = Know your target audience
1
2
3
Analytical research (report)
about real target audience
Includes client segmentat...
KYTA examples
Affinity btw different products
Affinity btw different traffic sources
Segments
Need for a partner who can give
identifiers that can be put into
ad management system:
• Google Ads,
• Yandex.Dir...
Segments – example of unit-economy
10M ID base (adult population of Russia ~ 100M)
High probability of credit approval ~ 4...
13
Decision model
IDEA
build a ML-model which
predicts:
How to make
an MVP?
SMS follow-up
is an option!
Which offers for t...
Decision model
How to make
an MVP?
Data includes:
• Data from credit
bureaus
• Questionnaire
• UTM_labels from ad
• Behavi...
Other projects with other businesses
Scoring for:
• default risks
• income estimation
• fraud-scoring
Scoring of companies...
16
Data Science helps increase business performance on all stages of customer’s lifecycle by several
times
But for small b...
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Data-driven cases for business amplification in modern digital banking by Nikita Pustovoytov

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Presentation delivered at the Kriek 2nd Amsterdam Fintech Forum, 6-7 June 2019 | www.kriek.co | www.amsterdamfintechforum.com

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Data-driven cases for business amplification in modern digital banking by Nikita Pustovoytov

  1. 1. Data-driven cases for business amplification in modern digital banking Nikita Pustovoytov Head of Data Science BCS Bank
  2. 2. Data Science Team* Retail NeoBank BCS Structure Wealth Management *business-oriented department
  3. 3. Marketplace is huge Low approval rate Uncertainty Distrust average approval rate in banks and microfinance institutions respectively is 55% and 22% 14% NPS Odobrim researched credit ecosystem from user's side and judgement is depressing: credit application can be characterized by COMPLEXITY and OPACITY 9 765 625 000 000 000 000 000 000 000 000 000 000 offers average amount of credit applications respectively is < 2 и <5 Inspired by the concept of top market player in service and retail we get Odobrim.ru
  4. 4. - innovative offer aggregator Odobrim.ru — the first credit marketplace got banking license Started at Q’4 2018 service successfully get over 180 000 clients Partnership with 50+ leading banks and microfinance institutions managing more than 150 offers Uniform questionnaire Request by API Bank take a decision at first, then notify client Sign online Aim in only 6 fields to fill 100% of leads are verify It’s necessary for client to be aware of commit from partner Negotiations in office stay in past
  5. 5. Basic economy • Organic (zero-cost, but unscalable) - too small because of a young and unknown project • Performance (scalable with price increase effect, vulnerable to market events) Paid search (cost) Adsense (cost) • Partner Revenue share No initial cost but less income Hard to find a partner before you get a success story Traffic sources (cost) Income • Payment is for issue, usually Issue = (bank approves) & (person takes under approved credit amount and rate conditions) 0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 4,00 CPC, relative to forecast in Q3'18
  6. 6. Data Science – steroids for business 6 Which audience to acquire? Which new ecosystems do customers really need? How to increase LTV? Churn prediction + whom and how to retain? Where? How? Next Best Offer How to choose tariffs? How to develop ecosystem? Providing new products into client’s ecosystem Boosts business on all stages of client’s customers’ lifecycle Client Acquisition Increase revenue from current customers Data-driven consulting
  7. 7. leads Marketing platform KYTA – Know Your Target Audience 7 Initiatives and their influence on sales Ad Media Analysis Segment Sources1. Partners 2. DMP platforms 3. Social network 4. Settings in Ad campaign Social network, MyTarget, Yandex, Google Showcase 1. Social networks Data 2. Credit bureaus 3. DMP platform Data 4. Credit default model (own scoring model) 5. Approval model Credit issued Platform with correct landings TV-Channels, mass media, celebrities/ microinfuencers, thematic groups Follow-up offers CR traffic-claim CR claim-issue Decision model for sorting offers Creatives XSell € € € € € €
  8. 8. Key challenges for Data Science and Odobrim Overall CR (traffic issue) < 1% Latency btw claim and issue notification – up to 55 day At the beginning Cost/Income is huge Cost/Income influences scaling • While it’s > 1 business is not willing to get big traffic to decrease losses • Traffic is low low number of positive outcomes for machine learning • Model becomes biased Mobile traffic share > 80% only first offers in search results get traffic Sloooow release speed due to corporate and banking rules
  9. 9. 9 KYTA = Know your target audience 1 2 3 Analytical research (report) about real target audience Includes client segmentation Includes data-driven comparison of customers: • On different stages of sales and lifecycle • Between different products and tariff plans • With other players on the market • With historical periods • Different segments with each other Affinity Cash credit vs micro-loan
  10. 10. KYTA examples Affinity btw different products Affinity btw different traffic sources
  11. 11. Segments Need for a partner who can give identifiers that can be put into ad management system: • Google Ads, • Yandex.Direct, • Mail.ru MyTarget Time-sensitive signal that user is willing to take a credit? Our partner’s data didn’t have such a signal Payments: • Partner for providing data • DS Team salary to build segments • Marketing budget 2 types of ads: • Paid search – the user is already searching for credit and segment doesn’t dramatically • Adsense/Yandex Ad System
  12. 12. Segments – example of unit-economy 10M ID base (adult population of Russia ~ 100M) High probability of credit approval ~ 4M Ad IDs (email, phone) ~ 1M Hit in Ad manager ~0.8m How much of them want to take credit this week??? • 3-5%? • CTR ~ 2.5% Traffic ~= 40 000 Issued ~= 400-800 Income ~ 10-20K EUR Payment to partner is proportional to 1M IDs… LOSS But if you have an ability to communicate with them, the case has chances to become successful The loss gap – from traffic to claim
  13. 13. 13 Decision model IDEA build a ML-model which predicts: How to make an MVP? SMS follow-up is an option! Which offers for the user will be approved? • And credit rates, amount and other parameters • In fact, it’s machine-learning used for reverse-engineering banks’ scoring models… Will the user agree to take credit with predicted conditions? Integration with Odobrim’s showcase can take months… • CR (Claim Issued) +55% • CR (SMS Claim) +10%, Overall CR +70%
  14. 14. Decision model How to make an MVP? Data includes: • Data from credit bureaus • Questionnaire • UTM_labels from ad • Behavior on site • External providers (TBD) Positive side effect: CR (Registered User->Claim) +40% 1 2 According to our estimates, CR (Claim Issue) is far better than our competitors have 3 Lead to agreement with a traffic-generating partner 45 Next step is Xsell system to increase LTV
  15. 15. Other projects with other businesses Scoring for: • default risks • income estimation • fraud-scoring Scoring of companies: • opening account • block transaction because of AML • credit scoring Lead generation from partner’s base with ultra-high conversion (via call-center) Churn Prediction Predictive analytics for call-center • Matching customer and operator/sales manager • Evaluation of operator’s work • Choosing the right candidat to hire Xsell and recommender systems Selection of micro-influencers and communities to promote brand
  16. 16. 16 Data Science helps increase business performance on all stages of customer’s lifecycle by several times But for small business great relative increase is not many € In our cases segments in performance must be many times more effective than “standard” performance for the unit-economy to be profitable • Maybe for hunting exceptional customers with high acquisition cost unit-economy may be profitable • But in call-center communication segments and predictive models have a great effect If you can’t get better traffic you can better convert your existing traffic • And increase LTV Increase in Conversions in sales process is vital for business success • It can be achieved by many ways from UX improvement to predictive models • Though Data Science can give tips even for UX improvement  Conclusion

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