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Predictive Analytics in the Legal Sector

Simply put, Predictive Analytics (PA) provides users with various scenarios by using algorithms and machine-learning to interpret data in order to provide a comprehensive picture of a situation, and predict logical outcomes.

PA supports quantitative research, making sense of big data and unwieldy data-sets, which can then support qualitative data using statistics, algorithms, and heuristics to predict outcomes.

The bottom-line is that predictive analytics’ tools can give lawyers a substantial edge.

Predictive Analytics is Critical for International Law

The PA push is being driven by ‘big data’, which in turn is driven by lower costs in artificial intelligence (AI) and computing power that can run algorithms to get real-time solutions in ways that could not be achieved until relatively recently.

This holds an obvious appeal for lawyers, though arguably provides more benefits in some areas rather than others. In case-law research and e-discovery, for instance, considerable benefits can be seen.

PA to help provide robustness in creating legal strategy. PA can help lawyers assess the merits of a client’s case, and provide analysis for offering sound legal advice.

Unique and proprietary data – such as case notes, records, models, resources, and expert profiles – can be leveraged to create an effective team, with the right data collated to tackle the workload.

Just as important as detecting trends and highlighting data patterns is the choice of who can best represent a client. PA can be used to decide the optimal composition of teams and ensure that all needs are covered by relevant expertise.

This can include deciding on what outside counsel, consulting, strategic partnership, or individual best fits the client’s needs.

However, another unique area is judicial research. At the forefront of Predictive Analytics are tools that track the litigation history of judges, lawyers, and law firms, including their win/loss rates for trials benchmarked against competitors.

Predictive Analytics can be used to track the success rates of different types of motions in individual courts and keep a database of who sues and gets sued most frequently. The products offer analyses of similar briefs filed by other firms, relevant case history, and judges’ citations, often down to the most cited paragraph.

From the Shadow Emerges Knowledge