Tag Archives: insurance
Murphy’s First Law of Bad Data – If You Make A Small Change Without Involving Your Client – You Will Waste Heaps Of Money
I have not used my personal encounter with bad data management for over a year but a couple of weeks ago I was compelled to revive it. Why you ask? Well, a complete stranger started to receive one of my friend’s text messages – including mine – and it took days for him to detect it and a week later nobody at this North American wireless operator had been able to fix it. This coincided with a meeting I had with a European telco’s enterprise architecture team. There was no better way to illustrate to them how a customer reacts and the risk to their operations, when communication breaks down due to just one tiny thing changing – say, his address (or in the SMS case, some random SIM mapping – another type of address).
In my case, I moved about 250 miles within the United States a couple of years ago and this seemingly common experience triggered a plethora of communication screw ups across every merchant a residential household engages with frequently, e.g. your bank, your insurer, your wireless carrier, your average retail clothing store, etc.
For more than two full years after my move to a new state, the following things continued to pop up on a monthly basis due to my incorrect customer data:
- In case of my old satellite TV provider they got to me (correct person) but with a misspelled last name at my correct, new address.
- My bank put me in a bit of a pickle as they sent “important tax documentation”, which I did not want to open as my new tenants’ names (in the house I just vacated) was on the letter but with my new home’s address.
- My mortgage lender sends me a refinancing offer to my new address (right person & right address) but with my wife’s as well as my name completely butchered.
- My wife’s airline, where she enjoys the highest level of frequent flyer status, continually mails her offers duplicating her last name as her first name.
- A high-end furniture retailer sends two 100-page glossy catalogs probably costing $80 each to our address – one for me, one for her.
- A national health insurer sends “sensitive health information” (disclosed on envelope) to my new residence’s address but for the prior owner.
- My legacy operator turns on the wrong premium channels on half my set-top boxes.
- The same operator sends me a SMS the next day thanking me for switching to electronic billing as part of my move, which I did not sign up for, followed by payment notices (as I did not get my invoice in the mail). When I called this error out for the next three months by calling their contact center and indicating how much revenue I generate for them across all services, they counter with “sorry, we don’t have access to the wireless account data”, “you will see it change on the next bill cycle” and “you show as paper billing in our system today”.
Ignoring the potential for data privacy law suits, you start wondering how long you have to be a customer and how much money you need to spend with a merchant (and they need to waste) for them to take changes to your data more seriously. And this are not even merchants to whom I am brand new – these guys have known me and taken my money for years!
One thing I nearly forgot…these mailings all happened at least once a month on average, sometimes twice over 2 years. If I do some pigeon math here, I would have estimated the postage and production cost alone to run in the hundreds of dollars.
However, the most egregious trespass though belonged to my home owner’s insurance carrier (HOI), who was also my mortgage broker. They had a double whammy in store for me. First, I received a cancellation notice from the HOI for my old residence indicating they had cancelled my policy as the last payment was not received and that any claims will be denied as a consequence. Then, my new residence’s HOI advised they added my old home’s HOI to my account.
After wondering what I could have possibly done to trigger this, I called all four parties (not three as the mortgage firm did not share data with the insurance broker side – surprise, surprise) to find out what had happened.
It turns out that I had to explain and prove to all of them how one party’s data change during my move erroneously exposed me to liability. It felt like the old days, when seedy telco sales people needed only your name and phone number and associate it with some sort of promotion (back of a raffle card to win a new car), you never took part in, to switch your long distance carrier and present you with a $400 bill the coming month. Yes, that also happened to me…many years ago. Here again, the consumer had to do all the legwork when someone (not an automatic process!) switched some entry without any oversight or review triggering hours of wasted effort on their and my side.
We can argue all day long if these screw ups are due to bad processes or bad data, but in all reality, even processes are triggered from some sort of underlying event, which is something as mundane as a database field’s flag being updated when your last purchase puts you in a new marketing segment.
Now imagine you get married and you wife changes her name. With all these company internal (CRM, Billing, ERP), free public (property tax), commercial (credit bureaus, mailing lists) and social media data sources out there, you would think such everyday changes could get picked up quicker and automatically. If not automatically, then should there not be some sort of trigger to kick off a “governance” process; something along the lines of “email/call the customer if attribute X has changed” or “please log into your account and update your information – we heard you moved”. If American Express was able to detect ten years ago that someone purchased $500 worth of product with your credit card at a gas station or some lingerie website, known for fraudulent activity, why not your bank or insurer, who know even more about you? And yes, that happened to me as well.
Tell me about one of your “data-driven” horror scenarios?
As I continue to counsel insurers about master data, they all agree immediately that it is something they need to get their hands around fast. If you ask participants in a workshop at any carrier; no matter if life, p&c, health or excess, they all raise their hands when I ask, “Do you have broadband bundle at home for internet, voice and TV as well as wireless voice and data?”, followed by “Would you want your company to be the insurance version of this?”
Now let me be clear; while communication service providers offer very sophisticated bundles, they are also still grappling with a comprehensive view of a client across all services (data, voice, text, residential, business, international, TV, mobile, etc.) each of their touch points (website, call center, local store). They are also miles away of including any sort of meaningful network data (jitter, dropped calls, failed call setups, etc.)
Similarly, my insurance investigations typically touch most of the frontline consumer (business and personal) contact points including agencies, marketing (incl. CEM & VOC) and the service center. On all these we typically see a significant lack of productivity given that policy, billing, payments and claims systems are service line specific, while supporting functions from developing leads and underwriting to claims adjucation often handle more than one type of claim.
This lack of performance is worsened even more by the fact that campaigns have sub-optimal campaign response and conversion rates. As touchpoint-enabling CRM applications also suffer from a lack of complete or consistent contact preference information, interactions may violate local privacy regulations. In addition, service centers may capture leads only to log them into a black box AS400 policy system to disappear.
Here again we often hear that the fix could just happen by scrubbing data before it goes into the data warehouse. However, the data typically does not sync back to the source systems so any interaction with a client via chat, phone or face-to-face will not have real time, accurate information to execute a flawless transaction.
On the insurance IT side we also see enormous overhead; from scrubbing every database from source via staging to the analytical reporting environment every month or quarter to one-off clean up projects for the next acquired book-of-business. For a mid-sized, regional carrier (ca. $6B net premiums written) we find an average of $13.1 million in annual benefits from a central customer hub. This figure results in a ROI of between 600-900% depending on requirement complexity, distribution model, IT infrastructure and service lines. This number includes some baseline revenue improvements, productivity gains and cost avoidance as well as reduction.
On the health insurance side, my clients have complained about regional data sources contributing incomplete (often driven by local process & law) and incorrect data (name, address, etc.) to untrusted reports from membership, claims and sales data warehouses. This makes budgeting of such items like medical advice lines staffed by nurses, sales compensation planning and even identifying high-risk members (now driven by the Affordable Care Act) a true mission impossible, which makes the life of the pricing teams challenging.
Over in the life insurers category, whole and universal life plans now encounter a situation where high value clients first faced lower than expected yields due to the low interest rate environment on top of front-loaded fees as well as the front loading of the cost of the term component. Now, as bonds are forecast to decrease in value in the near future, publicly traded carriers will likely be forced to sell bonds before maturity to make good on term life commitments and whole life minimum yield commitments to keep policies in force.
This means that insurers need a full profile of clients as they experience life changes like a move, loss of job, a promotion or birth. Such changes require the proper mitigation strategy, which can be employed to protect a baseline of coverage in order to maintain or improve the premium. This can range from splitting term from whole life to using managed investment portfolio yields to temporarily pad premium shortfalls.
Overall, without a true, timely and complete picture of a client and his/her personal and professional relationships over time and what strategies were presented, considered appealing and ultimately put in force, how will margins improve? Surely, social media data can help here but it should be a second step after mastering what is available in-house already. What are some of your experiences how carriers have tried to collect and use core customer data?
Recommendations and illustrations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations. While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
Last week at Informatica World 2013, Informatica introduced Vibe, the industry’s first and only embeddable virtual data machine (VDM), designed to embed data management into the next generation of applications for the integrated information age. This unique capability offers technology for banks and insurance companies to scale and improve their data management, integration, and governance processes to manage risk and ensure ongoing compliance with a host of industry regulations from Basel III, Dodd Frank, to Solvency II. Why is Vibe unique and how does it help with risk management and regulatory compliance?
The data required for risk and compliances originates from tens if not hundreds of systems across all lines of business including loan origination systems, loan servicing, credit card processors, deposit servicing, securities trading, brokerage, call center, online banking, and more. Not to mention external data providers for market, pricing, positions, and corporate actions information. The volumes are greater than ever, the systems range from legacy mainframe trading systems to mobile banking applications, the formats vary across the board from structured, semi-structured, and unstructured, and a wide range of data standards must be dealt with including MISMO®, FpML®, FIX®, ACORD®, to SWIFT to name a few. Take all that into consideration and the data administration, management, governance, and integration work required is massive, multifaceted, and fraught with risk and hidden costs often caused by custom coded processes or use of standalone tools.
The Informatica Platform and Vibe can help by allowing our customers to take advantage of ever evolving data technologies and innovations without having to recode and develop a lean data management process that turns unique works of art into reusable artifacts across the information supply chain. In other words, Vibe powers the unique “Map Once. Deploy Anywhere.” capabilities of the Informatica Platform accelerates project delivery by 5x and makes the entire data lifecycle easier to manage and eliminates the risks, costs, and short lived value associated with hand coding or using standalone tools to do this work. Here are some examples of Vibe for risk and compliance:
- Built data quality rules to standardize address information, remove or consolidate duplicates, translate or standardize reference data, and other critical information to calculate risk within your ETL process or as a “Data Quality Validation” service in upstream systems
- Build rules to standardize wire transfer data to the latest SWIFT formats within your payment hubs as well as leverage the same rules in facilitating payment transactions with your counterparties.
- Build and execute complex parsing and transformation processes leveraging the power of Hadoop to handle large volumes of structured and unstructured data to analytics and utilize the same rules in downstream credit, operational, and market risk data warehouses.
- Define standard data masking rules once, and leverage it when using data with sensitive information for testing and develop as well as enforcing data access rights for ongoing data privacy compliance.
The “Map Once. Deploy Anywhere.” capabilities inherent to Vibe drive:
- Faster adoption of new technologies and data – Banks and insurance companies can take rapid advantage of new data and technologies without having to know the details of the underlying platform, or having to hire highly specialized and costly programming resources.
- Reduced complexity through insulation from change – When data type, volume, source, platform or users change, financial institutions can simply redeploy their existing data integration instructions without re-specification, redesign or redevelopment on a new integration technology – like Hadoop.
Vibe is NOT a new product offering. It is a unique capability that Informatica supports through our existing platform comprised of our Data Integration, Data Quality, Master Data Management, and Informatica Life Cycle Management products. Whether it is Dodd Frank, Basel III, FATCA, or Solvency II, with Vibe, banks and insurance companies can ensure they have the right data and increase the potential to improve how they measure risk and ensure regulatory compliance. Visit Informatica’s Banking/Capital Markets and Insurance industry solutions section of our website for more information on how we help today’s global financial services industry.
The need to be more customer-centric in financial services is more important than ever as banks and insurance companies look for ways to reduce churn as those in the industry know that loyal customers spend more on higher margin products and are likely to refer additional customers. Bankers and insurers who understand this, and get this right, are in a better position to maintain profitable and lasting customer loyalty and reap significant financial rewards. The current market conditions remain significant and will be difficult to overcome without the right information management architecture to help companies be truly customer centric. Here’s why:
- Customer satisfaction with retail banks has decreased for four consecutive years, with particularly low scores in customer service. Thirty-seven percent of customers who switched primary relationships cited in an industry survey showed poor customer service as the main reasons.
- The commoditization of traditional banking and insurance products has rapidly increased client attrition and decreased acquisition rates. Industry reports estimate that banks are losing customers at an average rate of 12.5% per year, while average acquisition rates are at 13.5%, making acquisitions nearly a zero-sum game. Further, the cost of acquiring new customers is estimated at five times the rate of retaining existing ones.
- Switching is easier than ever before. Customer churn is at an all-time high in most European countries. According to an industry survey, 42 percent of German banking customers had been with their main bank for less than a year. As customer acquisition costs running between of €200 to €400, bankers and insurers need to keep their clients at least 5 to 7 years to simply break even.
- Mergers and acquisitions impact even further the complexity and risks of maintaining customer relationships. According to a recent study, 17 percent of respondents who had gone through a merger or acquisition had switched at least one of their accounts to another institution after their bank was acquired, while an additional 31 percent said they were at least somewhat likely to switch over the next year.
Financial services professionals have long recognized the need to manage customer relationships vs. account relationships by shifting away from a product-centric culture toward a customer-centric model to maintain client loyalty and grow their bottom lines organically. Here are some reasons why:
- A 5% increase in customer retention can increase profitability by 35% in banking, 50% in brokerage, and 125% in the consumer credit card market.
- Banks can add more than $1 million to the profitability of their commercial banking business line by simply extending 16 of these large corporate relationships by one year, or by saving two such clients from defecting. In the insurance sector, a one percent increase in customer retention results in $1M in revenue.
- The average company has between a 60% and 70% probability of success selling more services to a current customer, a 20% to 40% probability of selling to a former customer, and a 5% to 20% probability of making a sale to a prospect.
- Up to 66% of current users of financial institutions’ social media sites engage in receiving information about financial services, 32% use it to retrieve information about offers or promotions and 30% to conduct customer service related activities.
So what does it take to become more Customer-centric?
Companies who have successful customer centric business models share similar cultures of placing the customer first, people who are willing to go that extra mile, business processes designed with the customer’s needs in mind, product and marketing strategy that is designed to meet a customer’s needs, and technology solutions that helps access and deliver trusted, timely, and comprehensive information and intelligence across the business. These technologies include
Why is data integration important? Customer centricity begins with the ability to access and integrate your data regardless of format, source system, structure, volume, latency, from any location including the cloud and social media sites. The data business needs originates from many different systems across the organization and outside including new Software as a Service solutions and cloud based technologies. Traditional hand coded methods and one off tools and open source data integration tools are not able to scale and perform to effectively and efficiently access, manage, and deliver the right data to the systems and applications in the front lined. A the same time, we live in the Big Data era with increasing transaction volumes, new channel adoption including mobile devices and social media combined generating petabytes of data of which to support a capable and sustainable customer centric business model, requires technology that can handle this complexity, scale with the business, while reducing costs and improving productivity.
Data quality issues must be dealt with proactively and managed by both business and technology stakeholders. Though technology itself cannot prevent all data quality errors from happening, it is a critical part of your customer information management process to ensure any issues that exist are identified and dealt with in an expeditious manner. Specifically, a Data Quality solution that can help detect data quality errors in any source, allow business users to define data quality rules, support seamless consumption of those rules by developers to execute, dashboards and reports for business stakeholders, and ongoing quality monitoring to deal with time and business sensitive exceptions. Data quality management can only scale and deliver value if an organization believes and manages data as an asset. It also helps to have a data governance framework consisting of processes, policies, standards, and people from business and IT working together in the process.
Lastly, growing your business, improving wallet share, retaining profitable relationships, and lowering the cost of managing customer relationships requires a single, trusted, holistic, and authoritative source of customer information. Managing customer information has historically been in applications across traditional business silos that lacked any common processes to reconcile duplicate and conflicting information across business systems. Master Data Management solutions are purposely designed to help breakdown the traditional application and business silos and helps deliver that single view of the truth for all systems to benefit. Master Data Management allows banks and insurance companies to access, identity unique customer entities, relate accounts to each customer, and extend that relationship view across other customers and employees including relationship bankers, financial advisors, to existing agents and brokers.
The need to attract and retain customers is a continuous journey for the financial industry however that need is greater than ever before. The foundation for successful customer centricity requires technology that can help access and deliver trusted, timely, consistent, and comprehensive customer information and insight across all channels and avoid the mistakes of the past, allow you to stay ahead of your competition, and maximize value for your shareholders.
 2010 UK Retail Banking Satisfaction Study, J.D. Power and Associates, October 2010.
 “Customer Winback”
 Mortgage Servicing News
According to the IDC Financial Insights 2013 Predictions report, financial institutions across most regions are getting serious about updating their legacy systems to improve reduce operating costs, automate labor intensive processes, improve customer experiences, and avoid costly disruptions. Transforming a bank’s core systems or insurance provider’s main business systems is a strategic decision that has far-reaching implications on the firm’s future business strategies and success. When done right, the capabilities offered in today’s modern banking and insurance platforms can propel a company in front of their competition or be the nail in the coffin if your data is not migrated correctly, safeguards are not in place to protect against unwanted data breaches, and if you are not able to decommission those old systems as planned.
One of the most important and critical phases of any legacy modernization project is the process of migrating data from old to new. Migrating data involves:
- Ability to access existing data in the legacy systems
- Understand the data structures that need to be migrated
- Transform and execute one-to-one mapping with the relevant fields in the new system
- Identify data quality errors and other gaps in the data
- Validate what is entered into the new system by identifying transformation or mapping errors
- Seamlessly connect to the target tables and fields in the new system
Sounds easy enough right? Not so fast! (more…)
Like most Americans last week, I was glued to the news several days prior to Hurricane Sandy hitting landfall on the East Coast of the United States, hoping it would pass with minimal damage. Having lived in Hawaii and Florida for most of my life, I personally experienced three hurricanes and know how devastating these natural disasters can be during the storm and the hardships people go through afterwards. My thoughts are with all those who lost their lives and their belongings due to this disaster.
Hurricane Sandy has been described as one of the largest storms both in size and in property damage to homes and businesses. According to the New York Times, the total economic damage from Hurricane Sandy will range between $10 to $20 billion with insurance companies paying for $5 to $10 billion in insurance claims. At the high end of that range, Sandy would become the third-most expensive storm for insurers in U.S. history. As property, casualty and flood insurance companies prepare to face a significant wave of calls and claims requests from policyholders, I wonder what the implications and costs will be for these companies who lack reliable, trusted and accurate data which has plagued the industry industry for years.
Reliable, trusted, and accurate data is critical in helping insurance companies manage their business from satisfying regulatory requirements, maintaining and growing customer relationships, combating fraud, to reducing the cost of doing business. Unfortunately, many insurance companies, large and small, have long operated on paper-based processes to onboard new customers, manage policy changes and process claim requests. Though some firms have invested in data quality and governance practices in recent years, the majority of today’s insurance industry has ignored the importance of managing and governing good quality data and dealing with the root causes to bad data including:
- Inadequate verification of data stored in legacy systems
- Non-validated data leaks and data entry errors made by human beings
- Inadequate or manual integration of data between systems
- Redundant data sources/stores that cause data corruption to dependent applications
- Direct back-end updates with little to no data verification and impact analysis
Because of this, the data in core insurance systems can contain serious data quality errors including:
- Invalid property addresses
- Policyholder contact details (Name, Address, Phone numbers)
- Policy codes and descriptions (e.g. motor or home property)
- Risk rating codes
- Flood zone information
- Property assessment values and codes
- Loss ratios
- Claims adjuster estimates and contact information
- Lack of a comprehensive view of existing policyholder information across different policy coverage categories and lines of business
The cost of bad data can be measured in the following areas as firms gear up to deal with the fallout of Hurricane Sandy:
- Number of claims errors multiplied by the time and cost to resolve these errors
- Number of phone calls and emails concerning claims processing delays multiplied by the time per phone call and the cost per Customer Service Rep or field agents handling those requests
- Number of fraudulent claims and the loss of funds from those criminal activities
- Number of policy cancellations caused by poor customer service experienced by existing policy holders
- Not to mention the reputational damage caused by poor customer service
Having a sound data quality practice requires a well-defined data governance framework consisting of the following elements:
- Data quality policies that spell out what data are required, how they should be used, managed, updated and retired. More importantly, these policies should be aligned to the company’s goals, defined and maintained by the business, not IT.
- Data quality processes that involve documented steps to implement and enforce the policies described above.
- Specific roles including data stewards that represent business organizations, core systems (i.e. Underwriting Data Steward), or Data Category stewards who understand the business definition, requirements and usage of key data assets by the business.
Finally, in addition to the points listed above, firms must not discount or ignore the importance of having industry leading data quality software solutions to enable an effective and sustainable data quality practice including:
- Data profiling and auditing to identify existing data errors in source systems, during data entry processes and as data is extracted and shared between systems.
- Data quality and cleansing to build and execute data quality rules to enforce the policies set forth by the business.
- Address Validation solutions to ensure accurate address information for flood zone mapping and loss analysis
- Data Quality dashboards and monitoring solutions to analyze the performance and quality levels of data and escalate data errors that require immediate attention.
As cleanup activities progress and people get back on their feet from Hurricane Sandy, insurance companies should take the time to measure how well they are managing their data quality challenges and start looking at addressing them in preparation for these inevitable events caused by Mother Nature.
UK banks and financial regulatory bodies are currently being flooded with customer complaints about Payment Protection Insurance (PPI) and struggling to cope with the data deluge. The Telegraph recently reported on how complaints around mis-sold payment protection insurance are pouring in to the Financial Ombudsman Service at a phenomenal rate of more than 800 day, creating an enormous data backlog.
With the final bill for PPI expected to top £8billion, banks are scrambling to increase the number of employees dedicated to claims and ramp up their IT systems. It is likely that some banks are now looking to sign outsourcing contracts, but there is uncertainty around this as the mis-selling of PPI is hugely complicated. (more…)
Today Informatica announced that, “an increasing number of insurance companies rely on Informatica to address their unique challenges of integrating disparate data from a multitude of channels including adjusters, brokers, service providers, underwriters and other related parties.” I thought that two points were worth highlighting here:
Recent announcements by the European Parliament to delay Solvency II implementation deadlines to 2014 are in the headlines as European insurers are seen as being ill-prepared for the minimum capital requirements that will be brought in by Solvency II regulation. A big reason for this stems from the data requirements and challenges companies face to ensure proper regulatory reporting and accurate risk calculations to guarantee compliance.
Complying with Solvency II has the same level of data challenges as did Basel II in the global banking industry as insurers set out to improve how they monitor and measure risk. Many are investing in risk scoring systems, data warehouses, business intelligence, and analytic applications to support their needs. Unfortunately, years of standalone business units, legacy underwriting, policy management, claims, and pricing systems, lack of proper technology to integrate, govern, and share critical data present critical business issues may further delay companies from meeting even these new deadlines. (more…)
A Webinar Series with Aite Group and Informatica
Informatica, Aite Group, and Financial industry thought leaders just launched a series of webinars on hot trends in Insurance, Banking and Capital Markets. The issues by industry include:
- Master Data Management has become critical to customer retention and effective cross selling and upselling
- ACORD based integration is critical to the success of Master Data Management because of the increased adoption of ACORD internally to integrate data from disparate systems into an MDM hub
- In Wholesale Banking:
- Payments infrastructure is getting increasingly complex due to mergers, increasing payment channels and types
- Payments integration, as a result, is becoming increasingly important to integrate the new payments hubs with legacy systems that will continue to operate or exist from pre-merger days (more…)