apidays LIVE LONDON - The Road to Embedded Finance, Banking and Insurance with APIs Enriching Decision with News Data Miguel Ballesteros, Customer Solutions Engineer at Dow Jones

1. Enhance Your Decisions
with News DataMassively extract value from reliable publications

2. |
● Computer Scientist + Data Scientist
● Translating business needs to technical
solutions
● Based in Barcelona, and covering EMEA
miguel.ballesteros@dowjones.com
| Who am I?
Customer Solutions Engineer

3. | Why News?

4. | News data have proved valuable to...
● Better understand the business
context
● Assess the risk of the whole
economy or even particular entities
● Monitor the changing conditions
around entities
● Anticipating economic trends
● Anticipating the potential
performance of a security
● Deliver high-speed facts that later
are confirmed with reports or
official announcements (aka
“Nowcasting”)

5. | Quality News
● Follow strict editorial guidelines
● Reduce noise and misinformation
● Use reliable sources
● Some sources focus on Economic
and Financial events
Available behind a paywall !

6. | Uncertainty boosts news consumption
● Higher need to anticipate trends and risks
● Reliable source to explain behaviours

7. | Use Cases
● Credit Risk Assessment
● Credit Risk Monitoring
● Economic Research
● Due Diligence
● Compliance
● Securities Sentiment
● Insurance Risk Assessment
● ...

8. | News Aggregators

9. | Consolidate Multiple Premium Sources
● Simplify subscription management
● Simplify workflows
● Unify data structure
● Single point of access
● Data enrichment with unified criteria

10. | API - Data Schema
title
snippet
body
subject_codes
company_codes
region_codes
word_count
an
byline
modification_datetime
,m11,mcroil,c1522,m14,mcat,mnasdq,ncmac,nenac,neqac,nfiac,
,eurcb,eurcb,fed,fed,jyskb,jyskb,nbkden,nbkden,rryce,rryce,
,saarab,usa,asiaz,gulfstz,meastz,namz,wasiaz,
525
...
DJDN000020190920ef9k001ey
ISIN
CUSIP
SEDOL
Market+Ticker

11. | Traditional News Consumption

12. | Human-Driven Content Consumption
● Credit Risk Assessment
● Credit Risk Monitoring
● Economic Research
● Due Diligence
● Compliance
● Economic Risk Assessment
● Securities Sentiment
Aggregator DB Aggregator App

13. | Human-Driven Content Consumption
Disadvantages
● Not scalable in volume or speed
● Operational decisions require human
intervention
● Hard to combine with proprietary or
third-party data

14. | API-Based News Consumption

15. | Machine-Driven Content Consumption
Aggregator
API
Aggregator DB
Custom
Processing
● Risk Assessment / Monitoring
● Due Diligence
● Compliance
● Algorithmic Trading (No online HFT)
● ...
● Insurance Risk Assessment
● Platforms B2B4C (e.g. )
● Portfolio Management
● ESG Scoring (e.g. )

16. | API - Scenarios
Workflow
Automation
Text-Mining
Machine
Learning
Licensing is
different!
Low-Volume
API
High-Volume
API
Aggregator DB

17. | Low-Volume API Use Cases

18. | Low-Volume API Use Cases
● Corporate Credit Risk Assessment
● Corporate Credit Risk Monitoring
● Due Diligence
● Compliance
● CRM Integration
● Insurance Risk Assessment
● Platforms B2B4C (e.g. )
Workflow
Automation
Low-Volume
API
Aggregator DB

19. | Low-Volume API - Corp. Credit Risk Assessment
On-Demand
Data
Aggregator
● Simplifies the data collection for research
● Highlight relevant results according to
predefined criteria
● Increase Productivity
Proprietary
Data
Other Data
Providers
Low-Volume
API
Aggregator DB

20. | Low-Volume API - Corp. Credit Risk Monitoring
● Automatic process checking for specific
conditions
● Require attention only when necessary
Proprietary
Data
Other Data
Providers
Low-Volume
API
Automated
Monitor
Aggregator DB

21. | Low-Volume API - News Radar Visualisation
# c11="Corporate Strategy/Planning"
# c15="Financial Performance"
# c16="Bankruptcy"
entities = [ 'aapl', 'msft', 'tsla']
subjects = [ 'c11', 'c15', 'c16']
symbology = 'DJTicker'
nro = newsradar.get_ex(entities, subjects, symbology)
# nro contains volumes for different timeframes for
# each entity and subject
Not only article data!

22. | High-Volume API Use Cases

23. | High-Volume API Use Cases
NLP
Aggregate
Statistics
Business
Workflow
X-aaS
ML Models
● Algorithmic Trading (No online HFT)
● Portfolio Management
● Economic Research
● Platforms B2B4C
● Asset Management
● ESG Scoring (e.g. )
● Credit Risk Assessment / Monitoring
High-Volume
API
Aggregator DB*
Extracted
News DB

24. | High-Volume API - Portfolio Management
Alpha Signals
Risk Signals
Quant
Workflow
● Fits well in automated Quant Workflows
● Updated in near real-time
● Allows Machine-Processing
High-Volume
API
Aggregator DB* Extracted
News DB
ML Models + NLP

25. | High-Volume API - 2 Services
Now
Snapshots Streams
● Train Models
● Understand past events
● Summarise facts and events
● Identify patterns
● Backtesting
Delivered as files (AVRO, CSV, JSON)
● Predict
● Monitor in near real time
● Get notifications
● Robo-advice
● Calculate signals
Delivered as messages

26. | High-Volume - Long Running Operation Pattern
Snapshot
● Submits a job instead of
receiving an immediate
response
● Require to check for the
job until completed
● Results are collected by
downloading the
generated files
where_statement = "publication_date >= '2020-09-01 00:00:00' AND
language_code IN ('en')"
snp = Snapshot(query=where_statement)
snp.submit_extraction_job()
snp.get_extraction_job_results()
while(True):
if snp.last_extraction_job.job_state == 'JOB_STATE_DONE':
break
...
else:
time.sleep(const.API_JOB_ACTIVE_WAIT_SPACING)
snp.download_extraction_files()
# Files are downloaded to a folder named as the snapshot ID

27. | Resources

28. | More information at
This Slide Deck
http://bit.ly/hackzurichcscdata
Q&A Channel
#HackZurichCSCData
Snippets + Samples
https://github.com/dowjones/developer-platform
Developer Portal
https://developer.dowjones.com
Miguel Ballesteros
miguel.ballesteros@dowjones.com