Likewise, banks can be alerted when suspicious activity is flagged up by the data. On their own, financial institutions struggle to identify and disrupt trafficking-related transactions because their data models cannot distinguish money-laundering transactions from trafficking ones. Fortunately, together with data sharing, this all becomes possible. Financial data can be combined with existing NGO and open-source data to identify specific signs of trafficking and the risk level of particular transactions and accounts. With these results, banks can now validate and improve their machine-learning models and educate staff to better identify trafficking-related transactions.