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Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries.