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Fintech breakthroughs that identify,validate and augment customer data help financial services organizations head off fraud, operate within the rules


One of the most pressing issues facing the financial services industry today is the prevailing emphasis on security and fraud prevention. Big data and analytics are leveraging the power of the Internet, but also offering big, juicy plums for hackers, credit card fraudsters, money launderers and terrorists.

 

In response, many countries including the U.S. have established Know Your Customer (KYC) requirements, intended to guide financial institutions in heading off fraudulent transactions. A key way for banks to do this is to clearly Identify legitimate banking clients and their business relationships so the bad operators become more obvious and identifiable.

 

But it can be a challenge. Legitimate customers often have multiple banking relationships with a single institution, with identifying information stored in different formats in a multitude of databases. Various family members also may be account owners, but have different names and live at different addresses, possibly even in different countries.

 

Linking all these threads together while at the same time correcting name misspellings, standardizing mailing address formats, and parsing precise geolocation IDs can be the stuff of banking compliance nightmares.

 

Not doing this due diligence, however, can be catastrophic. To be hacked and have millions syphoned out of your customers' accounts is one thing, but to have the government ready and willing to fine you for failing various KYC tests can be just as damaging. Can you say "Loss of reputation and customers?"  

 

A WORLDWIDE PROBLEM


Most notably non-compliant of late have been banks based or doing business in India, but it's a worldwide problem. A New York-based broker-dealer recently was charged with KYC violations and for negligently allowing illegal trading by one of its customers. Perhaps the highest profile case most recently was Morgan Stanley Smith Barney running afoul of a variety of KYC violations. The main charge: Morgan Stanley failed to properly identify an assortment of "red flags" that signaled illegal activity.

 

Thankfully, technological breakthroughs increasingly are offering their own solutions. Just as the digital world has enabled bad players and victimized banks worldwide, technology is fighting back with sophisticated Know Your Customer tools.

 

A big step forward is the ability to accurately verify names and addresses. In a global world without borders, technology that verifies, cleans, completes and standardizes names, addresses, phone numbers and emails, and does so globally, immensely aids the process of knowing one's customers.

 

Technologies also exist that adds in missing contact fields, finds census and area-specific statistical details, and provides precise demographic information. When banks are able to combine census and area-specific details with accurate names and addresses, they'll know pretty closely if a variant player is really a customer or a bad guy.

 

KYC guidelines are very specific about risky areas prone to scams and schemes. A wide variety of countries are identified by the U.S. State Department for being prone to ignoring money laundering, tolerating suspicious transactions, and generally lacking adequate know-your-customer requirements. Ignoring this, in fact, was one of the issues that burned Morgan Stanley, permitting criminal activities to continue unchecked.

 

While IP lookup that identifies exactly where a digital communication has come from has been around for a while, new geocoding breakthroughs are able to convert IP addresses into precise latitude and longitude coordinates around the world. Most European and international identify cards also can be verified, along with mobile phone numbers and driver's license information.

 

One of the essential elements in all this is updating customer data. It's been estimated that accurate contact information deteriorates severely and regularly, and good customers who merely move across town can confuse inadequate screening processes and raise red flags (false positives) when it shouldn't. Today there are a variety of modules banks can use to update customer information quickly and accurately.

 

The bottom line is this: Financial services institutions now have lots of new reasons to love fintech technology that mitigates KYC concerns by identifying legitimate customers, and flagging the ne'er-do-wells before they can be effective.




Meet Melissa: Global Intelligence

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Melissa Data is officially now Melissa.

 

As we welcome our 32nd year in business, we are excited to announce an important change at Melissa Data. We've decided to drop the "Data" from our brand identity. We are simply Melissa now. This is part of a new branding effort to reflect Melissa's growth, and more importantly, the changes in the data quality space. While authoritative data sources power our products and services, we want to continue developing new solutions that deliver data-driven results for better business intelligence.

 

This forward thinking change is reflected in our new logo with the design emphasis on the "i" for intelligence. You will see this focus on intelligence in our new ID verification services, our industry-specific solutions to help with Know Your Customer initiatives, risk management and compliance, and in our robust customer data management and data integrations platforms.

And, you'll see it in our new website at www.melissa.com. 


Our goal with this new website is to provide our visitors an easier way to learn about Melissa's services and solutions. Immediately, you will notice streamlined menus, simple navigation, and quick access to the information you need.

 

We look forward to working together with all of our existing customers on more opportunities and better solutions for global intelligence. Please feel free to reach out and let us know how we can better assist you.

Better Marketing Starts with Better Data

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Improve Data Quality for More Accurate Analysis with Alteryx and Melissa

 

Organizations are under more pressure than ever to gain accurate contact data for their customers. When your consumer base ranges from Los Angeles to Tokyo, it can be challenging. Poor data quality has a critical impact on both the financial stability as well as the operations of a business. Verifying and maintaining vast quantities of accurate contact data is often inefficient and falls short of the mark. According to IBM, the yearly cost of poor data quality is estimated at 3.1 trillion in the U.S. alone.

 

Melissa's Global Address Verification and Predictive Analysis for Alteryx are the tools your business needs to grow. Download this whitepaper to find out how to achieve marketing success, while reducing the cost of doing business overall.

 

Learn how to:

  • ·         Better understand and utilize your big data for marketing success
  • ·         Build better relationships with customers with clean data
  • ·         Target the customers most likely to buy
  • ·         Cut down on undeliverable mail and save on costs

 

Download free whitepaper now:

http://www.melissa.com/resources/whitepapers/alteryx-better-marketing-data.html

 

How to Do It All with Melissa

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With Melissa, you can do it all - see for yourself with the brand new Solutions Catalog. This catalog showcases products to transform your people data (names, addresses, emails, phone numbers) into accurate, actionable insight. Our products are in the Cloud or available via easy plugins and APIs. We provide solutions to power Know Your Customer initiatives, improve mail deliverability and response, drive sales, clean and match data, and boost ROI.

 

Specific solutions include:

·         Cleaning, matching & enriching data
·         Creating a 360 degree profile of every customer
·         Finding more customers like your best ones with lookalike profiling
·         Integrating data from any source, at any time

Other highlights include: global address autocompletion; mobile phone verification; real-time email address ping; a new customer management platform; as well as info on a wealth of data append and mailing list services.

 

Download the catalog now:

http://www.melissa.com/catalogs/solutions/index.html

 

Building Your Business with Data Quality

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Your contact data is valuable, but do you know if it's up to its full potential? The quality of your data decreases without the proper maintenance. As a matter of fact, 50% of databases deteriorate after only two years. The bottom line is that your data's quality affects your business decisions and communications.

Here are 5 things you can do to ensure the quality of your data:


1. Profile

First, profile and analyze the health of your contact data. This helps minimize costs by pinpointing problems before you launch your next campaign.

 

2. Verify & Cleanse

Verify, correct, standardize, and update your data to ensure only valid information enters your database. This will increase response rates and improve your campaigns.

 

3. Dedupe

Identify, eliminate and consolidate duplicate records into one single, comprehensive record to get a more accurate view of your customer.

 

4. Enrich

Add missing elements to your contact data to complete your records from demographics to location intelligence and missing contact info (email, phone, social media, etc.) This will give you deeper insight into your data and improve your analytics.


5. Monitor

Continually monitor your data to ensure you have the most accurate data at your fingertips.


Melissa Data offers a full spectrum of solutions to meet all of your data quality needs, including profiling, cleansing, updating, matching ,deduping and enriching your global contact data. If you would like to add quality to your data, click here to learn more about our solutions!


By Natalia Crawford

How to Ensure Quality in Your Data's Life Cycle

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It is inevitable for data to go bad after a certain period of time. As a matter of fact, as much as up to 2% of records in customer databases become obsolete in just one month. There are various different things that cause this to happen including marriage, job change, divorce, or death.

This ends up affecting your database and therefore the information being used for analytics, communications, and more. This is why it is absolutely critical to have a system in place that will not only regularly clean up your data but also ensure its accuracy. Here are a few ways to ensure quality in your data's life cycle:

 

1.       Profile and Monitor Your Data

Profiling helps you identify weaknesses in your data which enables you to set-up repeatable processes to maintain good quality and monitor it over time.

 

2.       Verify & Cleanse Your Contact Data (Address, Name, Email & Phone)

When data is inaccurate and has not been verified it not only affects your marketing and sales strategy but it will result in loss of business money.

 

3.       Enrich Your Data

Add missing email, phone, and address info to extend the value of your data and therefore have a deeper understanding of your customer.

 

4.       Match Your Data

Get rid of duplicates and create a Golden Record to provide the most accurate view of your customer.  

Maintaining clean data is a good business decision that will ensure the best ROI while reducing costs. Watch our short video to learn more about full spectrum data quality! 


By Natalia Crawford

The Solution to Large Quantities of Data

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Every successful business scales and grows. As a result, so does its database. And as data grows so do problems including: faulty customer entry, breakdowns in data migration, disparate upstream legacy, and big data systems. All of these problems lead to one thing: bad data. High quality data is key to uncovering meaningful insights and new opportunities. Bad data, on the other hand, poses missed opportunities and bad decisions.

You don't have to be misinformed due to bad data. In fact, you can have smart data that poses smart insights and opportunities. Melissa Data has recently released a new tool, Data Profiler, to help companies dealing with large quantities of data that need a full spectrum of solutions that work. 

Data Profiler is made up of two modules:

Module 1: Discovery

An analysis of data before it is loaded into a data warehouse, ensuring consistency in formatting as well as entry on all fields to avoid problems downstream.

Module 2: Monitoring

The continual analysis of warehoused data that ensures consistent data quality over time. Data Profiler can be integrated in any place that data is streaming in or out and has the ability to assess the results of data quality efforts. Check out our video to learn more.

Gain business value from your customer data. Our data profiling helps you discover existing weaknesses in your database so you can create and enforce business rules on incoming records and maintain data quality. Profiling is an important step in empowering full lifecycle data management. For more contact data solutions visit our website.

New! Data Profile & Monitor Video

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The first step to gaining more business value from your customer data is to analyze the data you have, and determine what condition it's in. Data profiling from Melissa Data helps you discover existing weaknesses in your database so you can create and enforce business rules on incoming records to maintain data quality. 

Profiling is an important step in Melissa Data's full spectrum solutions to empower full lifecycle data management. Watch our video:


Learn more or try a demo

Flagship SSIS Developer Suite Now Enables Data Assessment and Continuous Monitoring Over Time; Webinar Adds Detail for SSIS Experts


Rancho Santa Margarita, CALIF - March 17, 2015 - Melissa Data, a leading provider of contact data quality and address management solutions, today announced its new Profiler tool added to the company's flagship developer suite, Data Quality Components for SQL Server Integration Services (SSIS). Profiler completes the data quality circle by enabling users to analyze data records before they enter the data warehouse and continuously monitor level of data quality over time. Developers and database administrators (DBAs) benefit by identifying data quality issues for immediate attention, and by monitoring ongoing conformance to established data governance and business rules.

Register here to attend a Live Product Demo on Wednesday, March 18 from 11:00 am to 11:30 am PDT. This session will explore the ways you can use Profiler to identify problems in your data.

"Profiler is a smart, sharp tool that readily integrates into established business processes to improve overall and ongoing data quality. Users can discover database weaknesses such as duplicates or badly fielded data - and manage these issues before records enter the master data system," said Bud Walker, director of data quality solutions, Melissa Data. "Profiler also enforces established data governance and business rules on incoming records at point-of-entry, essential for systems that support multiple methods of access. Continuous data monitoring means the process comes full circle, and data standardization is maintained even after records are merged into the data warehouse."

Profiler leverages sophisticated parsing technology to identify, extract, and understand data, and offers users three levels of data analysis. General formatting determines if data such as names, emails and postal codes are input as expected; content analysis applies reference data to determine consistency of expected content and field analysis determines the presence of duplicates.

Profiler brings data quality analysis to data contained in individual columns and incorporates every available general profiling count on the market today; sophisticated matching capabilities output both fuzzy and exact match counts. Regular expressions (regexes) and error thresholds can be customized for full-fledged monitoring. In addition to being available as a tool within Melissa Data's Data Quality Components for SSIS, Profiler is also available as an API that can be integrated into custom applications or OEM solutions.

Request a free trial of Data Quality Components for SSIS or the Profiler API.
Call 1-800-MELISSA (635-4772) for more information.

News Release Library


Data Profiling: Pushing Metadata Boundaries

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By Joseph Vertido
Data Quality Analyst/MVP Channel Manager


Two truths about data: Data is always changing. Data will always have problems. The two truths become one reality--bad data. Elusive by nature, bad data manifests itself in ways we wouldn't consider and conceals itself where we least expect it. Compromised data integrity can be saved with a comprehensive understanding of the structure and contents of data. Enter Data Profiling.


Throw off the mantle of complacency and take an aggressive approach to data quality, leaving no opening for data contamination. How? Profiling.

More truths: Profiling is knowledge. Knowledge is understanding. That understanding extends to discovering what the problems are and what needs to be done to fix it.


Armed with Metadata

Metadata is data about your data. The analysis of gathered metadata with Profiling exposes all the possible issues to its structure and contents, giving you the information--knowledge and understanding--needed to implement Data Quality Regimens.


Here are only a few of the main types of Generic Profiling Metadata and the purpose of each:

  • Column Structure - Maximum/Minimum Lengths and Inferred Data Type - These types of metadata provides information on proper table formatting for a target database. It is considered problematic, for example, when an incoming table has values which exceed the maximum allowed length.

  • Missing Information - NULLs and Blanks - Missing data can be synonymous to bad data. This applies for example where an Address Line is Blank or Null, which in most cases is considered a required element.

  • Duplication - Unique and Distinct Counts - This allows for the indication of duplicate records. De-duplication is a standard practice in Data Quality and is commonly considered problematic. Ideally, there should only be a single golden record representation for each entity in the data.


Other equally important types of Generic Profiling Metadata include Statistics for trends data; Patterns (ReqEx) allow for identifying deviations from formatting rules; Ranges (Date, Time, String and Numbers); Spaces (Leading/Training Spaces and Max Spaces between Words); Casing and Character Sets (Upper/Lower Casing and Foreign, Alpha Numeric, Non UTF-8) Frequencies for an overview of the distribution of records for report generation on demographics and more.


Metadata Revolution & New Face of Profiling

Right now the most powerful profiling tool for gathering Metadata is the Melissa Data Profiler Component for SSIS, which is used at the Data Flow level, allowing you to profile any data type that SSIS can connect with, unlike the stock Microsoft Profiling Component, which is only for SQL Server databases.

More importantly the Melissa Data Profiler offers over 100 types of Metadata including all the Generic Profiling Metadata mentioned here.

The innovative Melissa Data's Profiler Component gathers Data Driven Metadata, which goes beyond the standard set of profiling categories. By combining our extensive knowledge on Contact Data, this allows us to get information not simply based on rules, norms, and proper formatting. Rather, it provides metadata with the aid of a back-end knowledge base. We can gather unique types of metadata such as postal code, State and Postal Code Mismatch, Invalid Country, Email Metadata, Phone and Names.


Take Control

The secret to possessing good data goes back to a simple truth: understanding and knowledge of your data through profiling. The release of Melissa Data's Profiler for SSIS allows you to take control of your data through use of knowledge base driven metadata. The truth shall set you free!

For more information on our profiling solutions, please visit our website


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