BankWise

Inspiration

In today's rapidly evolving job market, the necessity to reskill our workforce for future-enabled skills is paramount, particularly for mid-career professionals. Mid-career professionals bring a rich reservoir of domain knowledge and priceless experience. When this is paired with future-enabled skills, it creates a significant impact for careers and organizations.

Among the hottest Future-Enabled skills is Data Analytics, a field ripe with opportunity for those looking to pivot their careers. With Financial Services offering attractive remuneration and a high demand for Data Analytics skills, it's a prime sector for mid-career switchers. However, the journey for mid-career professionals transitioning into Financial Services Data Analytics is fraught with challenges. The sector's vast landscape includes numerous sub-domains (like Credit Cards), each with its unique Key Performance Indicators (KPIs), processes, and data structures, necessitating a steep learning curve. Additionally, the high-stakes, delivery-focused environment of Financial Services demands that newcomers deliver tangible results from day one.

What it does

Enter BankWise, an AI-powered tool designed to smooth the transition for mid-career professionals into the realm of Financial Services. Unlike generic learning platforms, BankWise offers a tailored experience that allows users to focus on specific banking sub-domains, such as credit cards or retail banking. This customization extends to learning about the domain through data analytics lenses—covering KPIs, key processes, and typical data structures, along with peripheral skills like process re-engineering and application architecture.

BankWise's approach is hands-on and pragmatic, enabling users to delve into specific frameworks, such as how to conduct experimentation to enhance credit card offer uptake . Furthermore, it will identify the typical AI problem statements in sub-domain of interst , guiding users on identifying key data features, model evaluation, and best practices on applying the AI model for decision making.

How we built it

We first brainstormed on different ideas that tailor to new onboarding staff. Using partyrock tool, we build the app by allowing the new staff to enter his field or domain expertise. Then we ask GenAI models to output the key important learning points required for the new staff like KPI, key processes and data structures of the organization.

Challenges we ran into

As the tool is limited to general knowledge, we were not able to provide a more tailored experience based on different domains of the bank. We were also unable to put in corporate links that would be helpful to a bank organization due to confidentiality.

Accomplishments that we're proud of

Some accomplishments we're proud of is that we were able to quickly prototype and iterate the app to tailor it to a specific role that requires lots of information and where the pain point really is.

What we learned

We learnt that teamwork and leveraging on each teammate's strength for the project truly made it a success. There were very technical people, and strong business acumen people working on the project. This helped the team deliver quality deliverables for each step.

What's next for BankWise

Being a very feasible idea for the bank, we will POC it into our onboarding process with a more tailored experience, linking it to the organizations bank processes, KPIs and real data structures which will not be limited by the POC.

Click here for Future State Architecture of BankWise

Built With

• partyrock