Zoral Social Data Service (SDS)
“There is much talk of using social and web based behavioral data... but which data is predictive? and of what? SDS knows.”
Zoral SDS comprises a series of crawlers that extract and store publicly available, social data from a wide range of sources. SDS computes predictive, social features using its extensive NLP, and Sentiment Analysis library.
Zoral SDS is,
- artificially intelligent,
- cloud based,
- highly scalable
- API based.
SDS crawls multiple social networks and can be driven using flexible input criteria such as...
- ◦ company legal name
- ◦ aliases
- ◦ address(es)
- ◦ location(s)
- ◦ billing and shipping addresses
- ◦ industry classification(s)
- ◦ DUNS number (or similar)
- ◦ email(s)
- ◦ contact phone(s)
- ◦ SME's phone(s)
- ◦ website(s)
- ◦ Facebook account(s)
- ◦ twitter account(s)
- ◦ company description
- ◦ competitor’s information
- ◦ and others
Social networks crawled include,
- Google places (part of Google+)
SDS uses ZML, fuzzy matching algorithms. It crawls iteratively and concurrently using intelligent, dynamically generated search queries and fuzzy matching. In this way, it can zoom-in or converge on matched information across all social networks, eliminating non-matching social data.
Once extracted, social data is matched to the SME client information or input criteria. Metadata is added and the data is,
- aggregated and de-duplicated
- integrated to the SME's information and
- sent via output channel(s), along with the input criteria.
If a match is not found, or service time-out limit is reached, the SDS service returns the appropriate error or warning message(s).
SDS data is used to enrich the SME company profile and information and as an input to the ZML Social Score.
ZML Social Score
ZML Social Score uses data and features extracted from social networks SDS and historic, time-series, previously captured social data.
This is used to
- estimate the probability of default for SME’s at targeted marketing stage
- verify customers during KYC and customer onboarding
- boost the accuracy of default probability prediction for small/medium sized, companies at underwriting stage, in conjunction with other predictive data,
- verify and monitor ongoing SME customer performance throughout the customer and loan life-cycle
- estimate SME creditworthiness when applying for a loan renewal
- optimize retention marketing
periodically re-value the SME loan portfolio, and pro-actively manage
- pre-default or increased risk situations
- loan loss provisions.
This document is provided by Zoral Limited and its affiliated companies (“Zoral”) for informational purposes only, without representation or warranty of any kind. Zoral shall not be liable for errors or omissions with respect the information contained in this document. Product Specifications are subject to change without notice. The only warranties for Zoral products and services are those that are set forth in the express warranty statements in Zoral’s standard contracts for such products and services, if any. Nothing herein should be construed as constituting an additional warranty.
© Zoral Limited 2017