Author: Abhishek Nag

Design Thinking in Product Development
Design is the basic or hygiene towards a successful product development. The concept between Product Design and Design Thinking is extremely thin, although both look identical. Design thinking is more from end-to-end product development. In Product Design, the designers focus on the problem statement, end goals and product users.

The Design Thinking approach fits very well in product designing. Product design focuses in creating the right applications, matched with the right technology and thereby, extending to user to establish right experience. The product customer value is concluded on the basis of the experience across the customer decision journey. Design thinking approach involves creative and systematic approach to problem solving, keeping customer first.

Let’s understand how Design thinking approach is followed in the Product Management. To start with, the Design Thinking methodology focuses on inspiration, purpose, iteration and lesser ambiguity as the development begins. In other words, Design thinking shows the point of intersection between purpose, feasibility and viability. The Design Thinking is somehow close to agile methodologies.

There are six ways through which Product Managers apply Design Thinking:
1. Being Creative while undergoing research – In design thinking, it’s always wise to be as creative as one can be. During research several ideas come as an option. However, creativity pulls the best out of research and most often delivers differentiated customer experience.
2. Define particular occurrence during product development – Clearly communicate the challenges, purpose and users. Every users and challenges will have distinct persona and so, the journey varies. In product development, it’s a good practice to have the problem statement defined at basic level.
3. Building prototypes – Prototyping is a quick and inexpensive way to see how the idea works, so business can go back to the users and get their feedback.
4. Testing of Prototypes – Testing or feedback gives information to both business and development team on the usability and experience. Mostly, from the users’ reactions, business discovers the problem statement which we started to addressed is not there and there’s a different problem.
5. Adapt design thinking tools – Adoption of design tools facilitate the Design Thinking innovative process. Since design thinking comprises a set five stages process: empathizing, defining, ideating, prototyping, and testing, selecting the right tools is absolutely the most important thing for effective decision making and constructive communication in a multidisciplinary team. Tools can be physical, such as a pen, paper, and whiteboard, or software applications having rich graphics that compliment the Design Thinking process. The tools can also be used to help teams in adopting a new perspective on design tasks, to visualize the system’s complexity and depending on the design stage reflect a convergent or divergent view of design.
6. Retrospective of the complete process – Design thinking focuses on the human-centered goals because it focuses on providing deep and meaningful engagement with the end users. There are some problems that are not solvable. You might not find a technology that’s going to solve a particular problem, but what you want to do is discover that quickly. Design thinking makes it possible!
  So, the design thinking methodology doesn’t necessarily generate better ideas than competing methodologies. It’s just that this methodology allows you to test your ideas quickly to see which ones hold promise.
February 1, 2021
Role of Digital Product Engineering in Business Digitization

Introduction

All product companies globally are moving towards digitization. The fundamental purpose for product companies to move into digital is to deliver innovative, customer focused solutions to meet the end business purpose. In order to be globally competitive, it is important for businesses to develop and deliver innovative, customer-focused products and services. What’s important here is that this needs to be done at ever shorter intervals. To be on the road of digitization, it’s important to start from basics like purpose, data sources, role of data analysis and intelligence, purpose of digital tool and tools being used at the moment and most important is cyber security.
It’s natural when we start moving towards being digital, it involves several investments and such investment does not guarantees quick turnaround but it gives direction as to how strategically organizations resources can be used. While a company starts moving towards being digital, according to the recent PWC report, with digitization kicking in enterprise efficiencies will certainly increase by 19% over next half a decade. This means on average the time market will also drop by close to 20% and increasing productivity of people by another 20%.

Some advantages of digital product engineering

The advantage which companies can expect while investment in digital product engineering:

The digital product engineering has certainly increased and strengthens the relationship with customer purposes. Customized offerings to help client in accelerating revenue making is the core in digital product engineering. The major challenge is to achieve the end result with available resources with no additional expenses.
In order to ensure successful digital strategy, it’s important to introduce the concept of customer focus right at day 1. Another study conducted by PWC finds out that an introduction of such concept will increase the share of personalized solution offerings in the next five years by over 24%.
The success in the digital journey is heavily dependent on the use of data and AI.

Usage of Data Analytics & Artificial Intelligence

Digital tools have gained significant importance in digital engineering. Such tools include use of Artificial Intelligence and data analytics. Almost 66% of the companies are adapting digital use tools for co-creating products and services. This includes both with internal and or with external partners. Almost 50% of companies use digital technologies for process simulation and the development of digital prototypes.

Role of cyber security

While we talk about digital, it’s extremely important to understand the role of cyber security. Almost 71% of the companies entered into digital engineering do not have a matured process to mitigate cyber threats in data driven development environment. This is according to a recent study published by PWC. The cost implications of cyber attacks take toll on both business financials and client trust. Thus, security must be considered throughout the product development lifecycle and the protection of all data systems. Therefore, security should be layered throughout the product development lifecycle, built from the scratch and not towards conclusion.

Steps to implement cyber security in product development engineering
1. Start from basics or idea stage and keep the process evolve
2. Quality is a journey and not end destination
3. Train employee on data security, cyber threats and business implications
4. Design Experience with future in anticipation, as product will evolve more dynamically than business
5. Product or Data security at every level of development / delivery

Conclusion

So, we can clearly see that digital product engineering is extremely crucial for companies that are planning to go digital. So, do not be left behind! Contact info@embarkingonvoyage.com to know how we can work together to make your digitization journey successful!

January 18, 2021
AI/ML in service of an automated underwriting process

Introduction

Underwriters are the backbone of a sound lending process. They make sure that the risk taken (and every form of lending is a risk) is within the appetite of the firm. They also look at the other numerous checks that protect the process and the firm. The underwriting process is subtly and also vastly different depending upon which lending type we look at. For example, a residential mortgage underwriting needs to evaluate any additional borrowing that may be taking place in case of a refinance. A credit card underwriting will not involve that process but would rely more on the credit behavior of the customer. But with all these differences, there are some similarities with an overarching structure to the process. These similarities give us opportunities to assist the rigorous job underwriters have with the power of AI/ML.

More about the automated underwriting process

The first thing to be clear about is that there is no replacement of the underwriter. It is all but known that no amount of credit scoring, file verifications, AML checks are enough to predict the risk effectively. Seasoned underwriters understand the nuances that our systems might miss. They can think of triggering a last-minute re-scoring of the application or asking for just an extra month of payslips etc. We need that ingenuity and in fact, using AI/ML is only going to encourage it.

Using thorough data analysis, it is possible to ink out patterns of borrowing activity. Servicing systems generally save all borrowing data and subsequent payment performance for reporting purposes. It is this data that can be harnessed to generate important insights for a particular case. For example, a certain firm might find that customers who borrow a month from or past Christmas show higher signs of payment difficulty, despite having no issues in their application. Is it because of higher spending around that time that they need them months to adjust with? That is a question that financial experts can answer. But the job of the tech is to show that such a pattern may exist to begin with.

Imagine the power with the underwriter’s disposal, if a similar customer like in our example has a little warning ticker for the underwriter saying, “This customer may face payment difficulties”. That should be enough for the underwriter to go on a more thorough research before signing it off. Or the underwriter may choose to change the product just in case. There are numerous possibilities depending upon how the data pans out.

More applications of automatic underwriting

Automatic underwriting also has application in the AML world. Most AML checks are with historical evidence- by looking at fraud registries and flagging a person previously known to have engaged in suspicious activity. But AI/ML can to a great degree, successfully predict fraudulent behavior. The concept is again the same- using the data which we already painstakingly store. The patterns are what may surprise us and show us insights that we never thought of incorporating in the process.

These are just a few examples of how AI/ML can successfully create powerful interventions in the underwriting world, leading to an automated and more thorough underwriting process.

December 23, 2020