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I dont see any such inference even from public models. However, such insights even though can have a basis that cannot be given much weigt in a decision in traditional underwriting. Moreover, getting indicators that usually is not possible through AI/ML is a reality already. These help push the models far left to the value chain and assist in taking better decisions like an Income estimation.

The first Data Quality challenge is most often the acquisition of right data for Machine Learning Enterprise Use cases.

Even though the business objective is clear, data scientists may not be able to find the right data to use as inputs to the ML service/algorithm to achieve the desired outcomes.

As any data scientist will tell you, developing the model is less complex than understanding and approaching the problem/use-case the right way. Identifying appropriate data can be a significant challenge. You must have the “right data.”

More broadly speaking, Coverage, can be categorized under the Completeness Dimension of Data Quality and called the Record Population concept within the Conformed Dimensions standard. This should be one of the first checks to be performed before proceeding to other Data Quality checks.

Niall, Good thoughts articulated well! What caught me in your article is the phrase " To do this, certain protocols and rules need to be put in place to ensure good governance over this process." To ensure this we need to progress on Governing the data in the cloud as well. Your further statements clearly bring out the classifications of data to be hosted in accordance to their risk profile.

This fundametally requires a change to approach in which data is logically and physically classified. http://www.dataversity.net/integrate-data-privacy/ is an article that briefs on how we can define requirements for data to be hosted on various platforms including cloud.

Nice perspective. In most organizations today; data is considered an Enterprise asset. It is no surprise if we see the value of data included in the balance sheets of business. Regulations like BCBS 239, GDPR, EU No 1024/2013, EMIR, MiFID2, as you rightly stated are the primary drivers that created a need to manage risks and value arising from data and its operations. Though, other drivers that are necessitating the needs include strategy changes, business model changes, and advancements in technology.

I don’t see any specific time when cost cutting should be triggered. In my view cost optimization is an ongoing enabler in any organization. The existence of Data Governance and Data Risk management functions either centralized or distributed, based on the organization need, will help in realizing the complete value of data. This will further assist in simplifying the data landscape while rationalizing it, thus reducing operating costs associated with data operations and data related issues. This also enables new business discovery.

My final thought is that Business should own their data, the value it creates and the loss related to its risks. Organizations should enable, empower and enforce data driven culture, responsibilities and accountabilities of data ownership to make your thoughts feasible.

For further reading, you can refer http://dataassociation.net/dablog/call-it-a-success-by-integrating-risk-management-into-your-data-governance

Thanks Koen for your views!

Agree with you that simple assignment/identification of data owners does not allow for sustainable operation of governance services. Data Owners should rather be empowered to perform their responsibilities, enabled with self service capabilities of Governance tools, prime promoters of awareness related to Governance services and the list goes on as in the article above.

Often, the assessment plan for the governance services lacks taking a holistic view of Data Owner activities into consideration. Bob has put good approaches that can be leveraged in identifying data owners to start with. We in fact need to overcome specific challenges to be able to orchestrate more sustainable services that we should be more inclined towards.

Data is an enterprise asset as its relevance in decision making and Business Value articulation is increasingly discovered.

There are around 10,000 to 15,000 variables that can be used by scoring models that weigh differently.

There is a study which states that financial institutions reduce risk of default by 10%-20%. While some traditional banks are disrupting their business models in using these advanced services, some banks are planning to catch up. This shows the paradigm shift leading to economic dependence on the data analysis.

While we do understand that these advanced scoring capabilities assist lenders who do not have access to Credit Bureaus, I would like to see the capabilities assisting financial institutions lend to customers in the sphere beyond Payday and student loans.
Further reading : https://www.finextra.com/blogs/fullblog.aspx?blogid=10002 

Appreciate your thoughts on the innovation on Banking in Social media and the structured way in which you articulate that into the paper.

There is a regulatory need behind the context in my blog, for which I would suggest reference to the mortgage regulatory landscape - The way forward. CFPB in US stands strong, emphasizing banks on changing their credit and lending policies. Banks do have regulatory preparedness in their roadmap, as they are expecting further changes to credit policies depending on the market conditions.

The context in my blog refers to the use of Enterprise and Social data to complement the FICO and the custom scoring models that banks currently use to score applications. A solution/features in these lines would cater to complement the existing model rather than replacing it.

Banks have enormous enterprise data which P2P lenders are in dearth. These data stores serve as a golden source for insight generation. Models that utilize enterprise data along with Social data tend to be far more improvised than traditional models and models that bank only on social data. I was referring to pay day lenders which differ from the P2P lenders in the business model that they operate. But, what remains same are the technology advancements providing enriched, gamified customer experience.

It is a good insight that you brought forth from the blog you have mentioned. Tim brings an impression of banks having a software alliance with reputable P2P lenders that would be advantageous to banks. This would be helpful in entering emerging markets, gaining market share and reducing costs. I don’t consider all payday lenders to be struggling; I suggest reference to some industry leaders which boast a default rate of less than 10% when compared to 30% in the markets they are operating. Further, they are doing well on revenue generation and net profits.

As I write, Credit models of banks are undergoing changes based on their current and regulatory needs. If you have a requirement that would entail a credit scoring model to be improvised, I would be pleased to guide you with industry leading solutions that we offer.

I am still surprised at the fact that a consulting firm mandated its employees to have a middle name.