Data-As-A-Service to enable compliance reporting

Data as a service to enable compliance
reporting
Girish Juneja, CTO
October 7, 2014
© 22001144 AAllttiissoouurrccee LLaabbss.. AAllll RRiigghhttss RReesseerrvveedd.. Page | 1

Chairman: William C. Erbey
CEO: William B. Shepro
Employees: ~8,000
NASDAQ: ASPS
Market Cap:
$2.2 Billion
(Sept. 15, 2014)
Performance since August 2009
Separation from Ocwen®
CAGR Share Price:
(Through Sept. 15, 2014)
47%
CAGR Service Revenue:
(Through Sept. 30, 2013)
39%
Altisource Overview
 Separated from Ocwen in August 2009
 Created and separated RESI and AAMC in
December 2012
 Strong free cash flow
 Strong growth prospects in very large markets
© 2014 Altisource Labs. All Rights Reserved. Page | 2

Altisource Vision
Vision
To be the premier real estate and mortgage marketplace offering both
content and distribution to the marketplace participants
Mission
To offer homeowners, buyers, sellers, agents, mortgage originators and
servicers trusted and efficient marketplaces to conduct real estate and
mortgage transactions, and improve outcomes for market participants
Real Estate Marketplace Mortgage Marketplace
 Home Sales
 Home Rentals
 Home Maintenance
 Mortgage Originations
 Mortgage Servicing

State of the Business: Servicing
COMPLEXITY
- Meeting borrower
/customer expectations
- Elevated scrutiny of
borrower interactions
- Proliferation of servicer
products
- Reporting requirements
Increased Risk
Increased costs
Increased penalties
and fines
Decreased customer
satisfaction
COMPLIANCE
- Velocity of new and
changing rules
- Magnitude of financial
exposure
- Existing technology limits
compliance capabilities
CHANGE
- Lack of end-to-end visibility
- Rigid and inflexible systems
- Volume and nature of data
interoperability between
data silos
Compliance

Future of Servicing
For servicers’ businesses to grow, a modern servicing platform must be:
Flexible and
Adaptable
to easily and cost effectively
respond to evolving market and
business dynamics
Scalable and
Automated
to enable cost effective business
growth
Interoperable
to seamlessly interface with third
party apps and other software
platforms
Compliance
Centric
to meet ever-changing regulatory
mandates
Analytical
to drive continuous improvement
and manage risk

Common Foundational Layer
Customer Experience
Menus & Navigation API Management Caching DMZ Gateway
Identity Mgmt
Single Sign-on
Multi-tenant
Authorization & RBAC
Compliance &
Entitlements
Security
Framework
Encryption
MFA Authentication
Access Governance
User Profile
Rules Mgmt
Workflows,
Business &
Compliance
Workflow Mgmt
Messaging
Notification &
Subscription
Rules,
Messaging,
Integrations
Search
3rd Party
Integrations
Metadata
Management
Master Data
Management
Reporting
Compliant
Auditing
Data
Management
Transactional,
Reporting &
Analytics
Data Archival
Warehousing
Data as a Service
Provisioning
Monitoring/Alerting
Backup/Restore
Configuration &
Customization
Elastic Performance
Multi-tenant
Operational
Framework
Availability/DR
Metering
High performance scala based framework
App Provisioning, isolation, multi-tenancy & Life-cycle
Service Registry
Multi-tenant Cloud Provider Independent Rapid Deployment PaaS
Deployment & Test Automation
Cloud Abstraction layer

Environment Overview
Financial Industry faces:
– Increasing regulatory requirements
– Increasing customer compliance requirements
– Regulatory & customers’ changing requirements need correlating data across sources
– Risk Analysis requires correlating internal and non-conforming structured external data sets
– Existing data stores unable to respond rapidly
Organizations need solutions that:
– Enable financial institutions to address changing regulations
– Enable automated compliance monitoring processes and systems
– Improve internal controls by retaining data lineage to unmodified source datasets
– Provide actionable & timely information from data
– Enable on-demand reporting on massive datasets based on schema defined with the
reporting request

The Traditional Enterprise
Data Warehouse approach
– Maintain data integrity by storing the organizational data with regulatory
information in respective data dimensions.
– Data modeling and the design of facts and dimensions are very critical to
the success of compliance data warehouse.
– The regulatory sufficiency needs to be maintained in the regulation
dimension of the compliance data warehouse.
– From data warehouse data is transmitted into a regulatory data mart.
– Transformation of base data elements and regulation rules are maintained
in the meta data.

Metadata, ETL & Core Model
Metadata
– Reflects the business and business
processes
– In EDW, all functionality is metadata
driven
Data definitions
• Source and Core model from technical
perspective
• business perspective
– Transformations & Aggregations
• Transformations to derive cleansed Core
data from source data
• Aggregations and de-normalizations to
create Access model data
Loading Service
– Reflects the business and business
processes
– In EDW, all functionality is metadata
driven
Data definitions
• Source and Core model from technical
perspective
• business perspective
– Transformations & Aggregations
• Transformations to derive cleansed Core
data from source data
• Aggregations and de-normalizations to
create Access model data
Access Model
– Data in the Access model is directly
traceable to the Core model
• De-normalized
• Aggregated
• Designed for query and access
performance.
• End user requirements
• Access restrictions and controls
– As a design decision, access model
objects can either be physical
structures, or structures materialized
on access.
Core Model
– Control
• Only approved, tested, and validated
processes can update data in the Core
model.
– Data Model
• Highly Normalized
• No redundancy
• Subject areas, loans, investors..
• Not optimized for apps
• target data formats post-cleansing
• It is optimized for efficiency and
correctness.

The Change/Update Process
Each Change involves the following steps:
– Update the extraction module
– Update the staging module
– Update the Transformation module
– Update the metadata
– Update the data repository
This process is tedious, involved, and brittle.

Enhancing with Big Data Technologies
We were driven to adopt big data technology for many reasons:
– Demand to analyze new data sources in an ever shorter timeframe
– Growth in data complexity
– Variety of data types
– Volume of data and inability to move it around due to time constraints
– Velocity of data generation, internal and external
– Veracity of data from multiple sources
– Growth in analytical complexity
– Increasing availability of cost-effective computing and data storage
The big reason for us was the frequent change of requirements due to changing business &
regulatory changes.
Spark is a more flexible platform

Data as a Service
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Data Mobilization View for Data Lake

Borrower Data Service
Request Details:
Service Name: Borrower Data
Context: Current/Cleansed & Conformed/History
Request Filter: <Borrower Name>, <Date Range>
Response Details:

Borrower Data Service

Mortgage Data Service
Request Details:
Service Name: Mortgage Data
Context: Current/Cleansed & Conformed/History
Request Filter: <Loan Number>, <Date Range>
Response Details:

Mortgage Data Service

Loan Default Event
Event Details:
Event Name: Loan Default
Context: Current

Loan Default Event

Data Lake vs. Data Warehouse
Feature Data Lake Data warehouse
Data Volume Extremely large
(Petabytes)
Large (Terabytes)
Access Methods NoSQL SQL
Schema Schema on read Schema on write
Scalability Scales horizontally Scales vertically
Hardware Commodity hardware Specialized hardware/
appliances
Data Structure Structured and
unstructured
Structured
Data Raw Cleansed/Aggregated

Data Lake Technology Stack
GraphX
Services/
Application Portals
Spark (DAG construct and execute engine)
RDD Instances/Schemas
3 rd Party
Drivers
Analytics Portals
Data Storage
Cassandra /HDFS/
Parquet
BI/ETL tools
ODBC/
JDBC
Spark
Streaming
External
Data Stores
In-house
API
Interactive
Mlib/
SparkR
Hive/HQL Spark SQL
In-house
Drivers
YARN

Benefits of Apache Spark based Data Lake
-
– Load data as its stored in the source system - no transformation needed
– Build structure on it, apply Hive external tables on this raw data
– Data sets built with our business logic
– The intermediate and final results saved back to data storages
– Working data sets saved as Parquet files
– Distinction between data view and update view
– When the data file changes in Hadoop or Cassandra, we have to update
the Hive or Schema RDD’s: then we are done.

Data Storage Access Layer
– Abstract the details of data accessing through contexts/drivers.
 Hive/HQL
 Spark Sql
 Cassandra driver for Spark
– Unify the data into RDD interfaces.
 SchemaRDD
 HadoopRDD
 CassandraRDD

Code Samples - Apply Hive Schema to Raw Data
Pour data
Into HDFS
Create
Hive
Schema
Use HQL
inside
Spark
SQL
Save
result in
Parquet
format
RDBMS’s
Excel Files
Documents
External Sources
Cluster Details:
16 VM’s
128 GB Memory
126 GB Disk

The Spark Cluster
App App Service Service Tool Tool … … … …
Spark Driver
Worker Worker Worker Worker Worker Worker … … … …
Data Data … … … … Data Data Data Data
Worker Worker Worker Worker Worker Worker … … … …
Storage Storage Storage Storage Storage Storage … … … …

Performance observations 10 18 Rows
4.5 hrs
48 minutes
1 min
Engineered
Solutions
Cores 128
Memory 2048 Gb
Disk 12 Tb
In-memory
Databases
Cores 160
Memory 2048 Gb
Disk 12 Tb
Spark Cluster
VM’s
Cores 128
Memory 2048 Gb
Disk 12 Tb

Challenges
– Open source Apache Spark, while very promising, has to mature
– Spark production deployment is complicated
– Security of data is not enterprise class, needs additional layers
– Tools eco system is still developing – BI Tools still in development
But..
– Done right has a lot of business value
– We are hiring engineers!

Q & A

Data-As-A-Service to enable compliance reporting

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Editor's Notes