Transforming Legacy Probate Research with Modern Technology

Probate is a complex and lengthy process in itself, but it becomes even more complicated when the heirs or beneficiaries are not known or when the will and testament of the deceased is missing. In such situations, legacy probate research comes in handy. However, this service comes with its fair share of challenges and outdated processes are one of the biggest issues.

Once, a leading firm in probate research faced the challenge of relying on an outdated, unsupported legacy application for managing family trees and genealogy information. Then, SBL partnered with them to modernize their system, aiming not just to meet their current needs but to provide a platform for future growth, including Artificial Intelligence AI-driven optimizations. Let us look at the challenges faced by the legacy probate research firms and how SBL helps overcome these issues through modern technology.

What Does Legacy Probate Research Mean?

First, let us understand what is legacy and probate. In legal terms, legacy refers to a gift of property by will or testament. Probate is the financial and legal process carried out after the death of a person to administer the will of the deceased and distribute the property and assets to the beneficiaries accordingly.

Legacy probate research refers to the service that helps find heirs for the estates that have no known heirs. This service is used when heirs are unknown or a will is missing.

The Challenge

When customers approached SBL, they were confronting an array of complications emanating from their legacy system. This system had not just aged but was fundamentally misaligned with the modern demands of probate research. These challenges were not just standalone issues; they interacted in a way that exacerbated the firm’s operational inefficiencies. Here are some of the challenges they faced:

Technological obsolescence

Outdate frameworks:- The software was built on outdated frameworks, which means it couldn’t be integrated with newer technologies.
Unsupported software:- The application was unsupported, leaving customers without a safety net for troubleshooting or updates.

Security concerns

Lack of 2FA:- The legacy system had minimal security features. In a domain where data privacy is paramount, this was a glaring gap.
Vulnerabilities:- Being outdated and unsupported, the software was increasingly susceptible to security vulnerabilities.

Operational hurdles

User limitations:- The legacy system did not support multi-user collaboration. This siloed approach was a bottleneck in a field that demands real-time, collaborative research.
Data migration:- Customers had amassed a wealth of data in GEDCOM file formats. The legacy system provided no straightforward way to migrate this data to modern formats or systems.

Scalability and performance

Limited scalability:- The system could not easily be scaled up to accommodate more users or data, stifling the firm’s growth prospects.
Performance issues:- Users frequently encountered lags and crashes, frustrating the research process and slowing down case resolution times.

Lack of flexibility

Inflexible reporting:- The older system had limited capabilities for generating reports, making it difficult to analyze data and make informed decisions.
Stagnant features:- The system had long ceased to receive updates, leaving users without the benefits of newer features like real-time collaboration or advanced data analytics.

The Solutions

Given the complex challenges customers faced, a mere update of the legacy system was out of the question. SBL embarked on a journey to construct a new, state-of-the-art solution that would not only address immediate issues but also be a platform for future innovations, including the integration of Artificial Intelligence technologies. Following are the solutions provided by SBL to deal with the challenges mentioned above.

Phase 1: In-Depth Requirements Analysis

Before diving into development, we conducted exhaustive interviews with key stakeholders. This allowed us to draft a comprehensive list of features, including:

Security Measures: 2-factor authentication, SSL encryption, and regular security audits.
User Role Management: Different levels of access are based on roles like Admin, Case Managers, and Researchers.
Data Representation: Advanced UI/UX designs to display complex family trees and individual profiles.
Real-Time Collaboration: Enabling up to 25 users to simultaneously work on a family tree.

Phase 2: Tackling Constraints and Risks Head-On

We recognized early on that ignoring the inherent constraints and risks would be detrimental. Hence, we devised several mitigation strategies:

Lock Feature: To avoid duplication, we implemented a feature allowing researchers to lock sections of the family tree they are working on.
Audit Trails: Every activity within the application is logged, providing a clear accountability chain.

Phase 3: Technology Stack Selection

In constructing a solution that is both robust and flexible, the choice of technology stack was critical. Each component was selected to maximize performance, ease of use, and future scalability.

Backend: Java was chosen as the backend language, given its proven reliability and extensive libraries. It provides the robustness needed for a complex application, especially in handling secure authentication and intricate data manipulations.
Frontend: Angular was selected for the frontend framework, known for its capacity to create dynamic, real-time user interfaces. Its compatibility with Java backend frameworks made it an ideal choice for seamless integration.
Database: MySQL was the database of choice, given its strong ACID compliance and ability to handle complex queries efficiently. It offers both the reliability and scalability required for a multi-user, data-intensive application like this.

Phase 4: Development and Testing

The agile methodology was followed, with regular sprints and client feedback loops. Extensive testing was conducted to ensure that the system met all specified requirements and could handle real-world demands.

Phase 5: Future-Proofing the System

Recognizing customer’s ambition and the ever-changing landscape of technology, we designed the application to be future-ready.

AI-Ready Architecture: The system is designed to easily integrate machine learning models that can predict potential missing links in family trees.
Scalability: From the database architecture to the codebase, every aspect of the application was built with scalability in mind.

The Results

After applying the solutions provided by SBL, customers reported following positive results:

10x Benefit: The new application provided a tenfold increase in efficiency and data accuracy compared to the legacy system.
Collaborative Work: Real-time collaboration was enabled, allowing up to 25 researchers to work simultaneously on a family tree.
Data Integrity: Advanced validation methods and audit trails ensured data consistency and accountability.
AI-Ready: The application is designed to be future-ready, with the ability to integrate AI-driven features.

Conclusion

As complicated as the legacy probate is, using proper and up-to-date technology helps probate researchers fulfill their purpose with accuracy and speed. Despite numerous challenges, there are solutions to upgrade the systems and processes used in probate research. As discussed above, SBL provides cutting-edge, state of the art solutions and a platform to help customers overcome the issues. SBL’s engagement with the customer stands as a testament to how modern technology can not only replace outdated systems but also add immense value to a business by being future-ready.