With LAB’s strict legal aid requirements, low bono and pro bono cases are currently taken up through connections with no central space to find such legal help.
Lawmatch bridges this gap by intelligently matching financially needy individuals with lawyers who are keen to do pro-bono or low-bono work.
Clients who have been referred by LAB would be able to sign up for the platform, ensuring we cater to the most appropriate demographic. By providing access tokens to our legal partners, we ensure the requirement of being financially needy is certain.
LawMatch uses the following criteria to ensure that our clients are matched to lawyers who best meet their needs:
- Lawyer’s expertise
- Budget
- Availability
- Preferred Languages
- Gender
If clients do not know what area of law their issues fall under, our NLP tool is able to identify the area of law by processing the client’s description of their case.
We used a bag-of-words (BoW) model. Features were extracted from case law for use in our ML model. We preprocessed the case law data by cleaning text, removing stop words and applying lemmatization. We used a BoW model and Tf-ldf for feature engineering. We then performed feature selection using a chi-square test. Finally, we trained the machine learning model using the Naive Bayes algorithm and applied it to obtain the area of law for a client’s case description.
On the clients’ dashboard, statistics regarding the average costs of similar cases are available to them, enabling them to make an informed decision on whether to proceed with engaging the lawyer.
By increasing access and providing clearer information, we hope the journey to a more equal and just society is within reach.