Want to make your nCino integration a breeze? It’s all in the data.

More financial institutions are moving their banking technology to the cloud so they can more quickly deploy services, reduce operational expenses, and make the loan process more efficient. As a result, adoption of nCino’s cloud-based Bank Operating System has been on a tear recently, not only in North America, but internationally as well.
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With more than 150 organizations signed up for it, nCino is becoming a strategic platform to help drive digital transformation in banking.  

Successful implementation requires understanding data and organizational dependencies in three key areas.

1. Architecting the integration fabric

The integration fabric is a combination of real-time enterprise services and a batch framework that connects nCino to the rest of the organization, based on upstream and downstream dependencies.

Getting the integration fabric right is critical to a successful digital transformation in the business. The underlying challenge is in making the right data choices and thinking through their long-term impact.  

Make your nCino implementation go more smoothly by addressing these top integration fabric challenges:

Designing real-time services

Challenges:
  • Design reusability into the architecture through a canonical data model, which views data and processes in the simplest possible form.
  • Use known industry standards for architecture acceleration and governance.
  • Create a decoupled architecture so you can more easily make future changes.
Considerations:
  • The Interactive Financial eXchange schema standard and the Banking Industry Architecture Network’s categorization of service names provide a system-agnostic blueprint that helps with all three challenges for designing real-time services with a robust service architecture.
  • For clearer governance in arriving at a canonical view of data – whether for batch integration or implementing applications – join forces with other work streams to create harmonious orchestration and data flows.
  • To create a decoupled architecture, use an operational data store or an integration hub to consolidate and standardize data used for services.

Designing for batch integration

Challenges:
  • Identify the system of record for critical data elements.
  • Normalize household and exposure debt calculations used in determining loan limits.
  • Resolve unique integration keys for records including Customer, Loans, and Collateral.
Considerations:
  • For each of the three pillars (Customer, Loans, and Collateral) the integration team must work through the implications of choosing one system of record over another. This is difficult when the requirements team isn’t done with all the use cases, especially in an agile implementation environment. This is where prior integration experience comes in, and data governance also plays a role.
  • Deciding on the right way to roll up household and exposure calculations has a wider impact on the organization. A typical bank has 12 - 17 ways of doing these roll-ups, and business teams can find it daunting to pick one. They need help from a domain expert in determining the downstream impact of the roll-up method chosen.
  • Surrogate integration keys that identify individual records are rarely identifiers of the logical entity of interest. The team should use a combination of approaches – consulting with the business, understanding use cases, and basic profiling and validation – to make the right decisions.

2. The role of enterprise data governance

An enterprise data governance organization, or enterprise data office, can guide and broker data decisions for the nCino implementation team, by consulting with other business stakeholders across the organization. 

The enterprise integration team provides the necessary profiling research to help support recommendations. The data governance council then evaluates what domino effects this could have in the rest of the organization and approves the appropriate recommendation.

The myriad of data quality issues faced by the nCino implementation team requires bringing in data governance or the data office. nCino is set up to enforce data quality rules for it to function properly, but don’t assume the systems of record feeding it with master data are set up for this.

One piece of the solution is to set up data quality monitoring and data visualizations as an ongoing exercise, in agile sprint mode. Synchronize those exercises with nCino releases and identify thresholds for quality (and data cleanup).

As a best practice, update the enterprise metadata repository with any impacted data elements, their mappings to source systems, and the decisions made, and synchronize the mapping documents at the end of each release.

3. Making your data decisions count 

Beyond integration, data decisions also can impact application design. Consider these three potential gotchas:

  • Finding the right level of customization. Many Salesforce (SFDC) experts, especially developers, are experts at coding on the SFDC platform and can write custom code. However, this can be a challenge when, instead of finding customer information in the designated object model, you must navigate through custom fields and account for spaghetti code.
    When adding custom fields for critical attributes, especially those describing the customer, run these through a governance process, capturing the reason why they can’t be accommodated in the standard model.
  • Dealing with credit actions on legacy products. Some legacy portfolios may be hit by unexpected credit actions, such as a client who asks to renegotiate terms on an old loan. This could require a redo of risk analysis and credit assessments, which means sending it back through some of the loan origination workflows.The choice is either to treat these as exceptions and channeled through a manual route, or factor in credit actions on legacy products in the new workflows. The data team can provide insights into how many of these to expect based on historical profiling.
  • Jump-starting requirements definition. An expert data integration team will perform data profiling work to familiarize themselves with source systems. As a result, they can help jump-start functional workshops based on their findings. This is a valuable source for identifying all the variations in workflows and loan profiles, based on historical analysis.Some interactive visualizations, perhaps in Tableau, can go a long way in making joint application design sessions productive, using the data to drive important scenarios, validating edge cases, and avoiding offline research. Use nCino adoption as the jump-starter to get your enterprise data organization in order, and leverage the team you’ve created to drive similar transformations not just in loan origination, but across your business.

Learn how ICC can help your bank achieve an advantage with innovation and data. Feel free to email me at smajumdar@icct.com with questions.

Read more about ICC Banking & Finance Solutions.

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